Search

The páramos of Boyacá in Colombia are earmarked for delimitation to prevent the expansion of the agricultural frontier and protect endemic flora that contribute to water provision for cities. A varied conservation toolbox will be used, including the creation of protected areas for re-wilding and the 'sustainable' transitioning of livelihoods identified as environmentally destructive. Agriculture and cattle livestock farming has been identified for transitioning. Despite the negative discourse related to livestock holding, this paper argues that small-scale agropastoralism contributes to re-peasantisation and provides the foundations for an agrobiodiverse conservation approach. Agropastoralism facilitates re-peasantisation through strong socio-economic networks, interconnected communities, the solidarity economy, and self-management of natural resources. Whilst, agropastoral mobility spatially binds social networks across large and disconnected spaces. Mobility is also fundamental to dynamic land access and pasture management, as it prevents over-grazing. This exemplifies how resilient socio-economic networks and mobile production strategies could be harnessed for agrobiodiversity, instead of land sparing and other sedentary 'green' economies. This paper makes conceptual contributions to 'autonomy' in re-peasantisation by empirically demonstrating the importance of mobile and flexible systems of production. It also makes a novel methodological contribution in applying a spatial lens that further unpacks how movement across the páramos facilitates autonomy and re-peasantisation. These themes are explored using interview data from 53 semi-structured interviews with key stakeholders and small-scale agropastoralists from across the páramos and field observation. The paper concludes by recommending a harnessing of agropastoral knowledge, to potentiate agrobiodiversity, for a more socio-ecologically just approach to farming and conservation in the páramos. [ABSTRACT FROM AUTHOR]

The paper reexamines an agent-based model of opinion formation under bounded confidence with heterogeneous agents. The paper is novel in that it extends the standard model of opinion dynamics with the assumption that interacting agents share the desire to exchange opinion. In particular, the interaction between agents in the paper is modeled via a dynamic preferential-matching process wherein agents reveal their preferences to meet according to three features: coherence, opinion difference, and agents' positive sentiments towards others. Only preferred matches meet and exchange opinion. Through an extensive series of simulation treatments, it follows that the presence of sentiments, on one hand, hardens the matching process between agents, which leads to less communication. But, on the other hand, it increases the diversity in preferred matches between agents and thereby leads to a better-integrated social network structure, which reflects in a reduction of the opinion variance between agents. Moreover, at combinations of (a) high tolerance, (b) low sensitivity of agents to opinion volatility, and (c) low levels of confidence, agents are occasionally drawn away from the consensus, forming small groups that hold extreme opinions. [ABSTRACT FROM AUTHOR]

This paper explores the knowledge network structure of foreign research literature by applying the qualitative comparative analysis (QCA) method to the field of information science and library science (ISLS) from the perspective of the cocitation of social network actors such as authors, institutions, countries, and literature, and it further reveals the future application trends of this method. [Method/process] Based on 86 journals in the ISLS field that were downloaded from the Web of Science using the QCA method, the social network analysis (SNA) method and the visual analysis tool Gephi are used to analyse the author cooperation network, the research institution cooperation network, the national cooperation network, the cocitation network, the cutting-edge trends, etc., of journal papers. The analysis shows that the QCA method covers a wide range within the field of ISLS, but the research topics involved in this field are not concentrated, and the author cooperation network has scale-free characteristics. The application of the QCA method is still dominant in European and American countries, and China, the USA, and Italy all play key roles in the national cooperation network. Finally, the institutional cooperation network has certain small group attributes. [ABSTRACT FROM AUTHOR]

Social network interactions and web/IoT-based applications have led to data getting generated at a very rapid rate. Amongst these, social networking sites continue to have a comparatively greater influence on the day-to-day life of an individual. Social networking sites are the main vehicles for disseminating information due to their inherent global outreach. Due to popularity of social network sites, every smart phone user, government, corporate and political parties are able to keep up to date with the latest information. However, this information may also contain some misinformation and/or disinformation, which could manipulate and influence the opinion of an individual or a group of individuals. In order to minimize misinformation and disinformation in such networks, a veracity assessment in the form of trust, needs to be computed for each and every data included in it, in order to ensure secure and trustworthy data availability for the users. Many researchers have developed models for assessing veracity in the form of trust for different social networking sites. This paper focuses on a detailed review of trust-based models in social networks. The paper also delineates how machine learning models have been used for trust assessment in social networks. [ABSTRACT FROM AUTHOR]

Purpose: The journey from shame to pride has been described as a founding and essential part of the modern LGBTQ movement. However, the tendency to treat shame as something that belongs to the past has been criticized by a number of queer theorists. The struggle to secure equal rights through normalization has also meant that certain topics that risk demonizing and stigmatizing LGBTQ people further have been neglected. Intimate partner violence (IPV) in queer relationships is one such topic. This paper explores a new perspective on queer shame through the topic of IPV in lesbian and queer relationships. Methods: Drawing on qualitative interviews with 25 people who have experienced violence in intimate lesbian/queer relationships in Sweden, this paper unpacks the concept of shame in empirical data to examine how shame operates on different levels when it comes to victimization and how it affects the help-seeking processes of such victim-survivors. Results: Many of the interviewees expressed that they continued to feel ashamed about their denial of violence or their own reactions to the abuse even years after the relationship had ended. An additional layer of shame and secrecy affected the violence and help-seeking processes for the interviewees' who lacked support from their social networks or who were not open about being in a queer relationship. The interviewees' accounts also contained strategies to resist and manage shame. Conclusions: The theoretical and empirical contributions of this paper sheds new light on how shame, victimization and queerness are entangled in the case of IPV. [ABSTRACT FROM AUTHOR]

This paper describes the Data Domotopia a 2300 + respondent self-administered web-based survey. It includes 100 + multi-purpose items about home-making and stillness in a moving world. We suppose that home-making can reveal coping strategies and resilience practices to make everyday life work – as home is a central location in people's activity-travel patterns. To describe this phenomenon, the concept of Domotopia is introduced, defining how people arrange, use, and experience their homes to cope with the pathologies of accelerated and liquid modernity (Bauman 2005). While the Data Domotopia is based on a mixed-method combining qualitative and quantitative material, this paper focuses mainly on the description of the questionnaire – which is organized into three interrelated layers: the dwelling, the dwellers, and the neighborhood. Each of these layers unfolds in functional, social, emotional and sensory components. The survey covers most of the contemporary issues related to home-making. This includes the domestic space and gender issues; the socio-spatial resources (mobility, action space, core, and wider social network); lifestyles, ideals, and residential aspiration; time pressures, time use, organization and stress; equipment, rules and arrangements; interpersonal relations, cohabitation and negotiation, dominance and power. Intakes on the Data Domotopia is given by two concrete cases about the time-space coverage of the habitual action space, and about inter-personal task allocation. These examples show the potential of the data to study domocentric stillness and resilience to urban pathologies. The data – aggregated to the infra-communal level – is available for research purposes. [ABSTRACT FROM AUTHOR]

Because of fast urbanization in developing countries, rural-urban youth migration has attracted several researchers to study this phenomenon. In the absence of an institutional formal framework or measures put in place to assist the rural youth migrants in their transition to urban livelihoods more so as self-employed workers in the new and challenging urban environment, this paper examines the role that social networks and social media play in enabling rural-urban migrant youth transition and integration into urban areas as self-employed workers. We used qualitative data collected through in-depth semi-structured interviews in Arua and Kampala, and we also conducted a focus group discussion in Arua city to complement the individual interviews. The research findings showed the continuous dependence of the youth migrants on social networks and social media in participation and belonging in the urban areas as self-employed workers. Our novel contribution to the literature on youth migrants and self-employment is that rural-urban youth migrants seeking self-employment mostly depend on social networks and social media in transition to the urban economy which necessitates a supportive framework by the government or city authorities for their transition and integration in the cities as self-employed workers. [ABSTRACT FROM AUTHOR]

Generation Z consumers are characterized by social networking and instant decision-making. The influence of different forms of electronic word of mouth (eWOM) on the consumption decisions of generation Z consumers is quite different. Based on information adoption model (IAM) and information transmission theory, the paper established the influence mechanism model of eWOM information structures on generation Z consumers' purchase intentions. A total of 815 valid questionnaires were collected for empirical test through a 2*2*2 between-subject situational experiment. The empirical results reveal that eWOM information structures influence generation Z consumers' purchase intentions through user perception. Among them, the eWOM information of diversified form, composite type, and hybrid type structure has a higher effect on perception credibility and usefulness than the eWOM information of single form, one-way type, and single type structure. Consumers' professional ability plays a partial role in moderating the influence of eWOM information structure on user perception. [ABSTRACT FROM AUTHOR]

The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure's impact. Second, detecting anomalies within a network's topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network's topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research. [ABSTRACT FROM AUTHOR]

The Friendship Paradox is a simple and powerful statement about node degrees in a graph. However, it only applies to undirected graphs with no edge weights, and the only node characteristic it concerns is degree. Since many social networks are more complex than that, it is useful to generalize this phenomenon, if possible, and a number of papers have proposed different generalizations. Here, we unify these generalizations in a common framework, retaining the focus on undirected graphs and allowing for weighted edges and for numeric node attributes other than degree to be considered, since this extension allows for a clean characterization and links to the original concepts most naturally. While the original Friendship Paradox and the Weighted Friendship Paradox hold for all graphs, considering non-degree attributes actually makes the extensions fail around 50% of the time, given random attribute assignment. We provide simple correlation-based rules to see whether an attribute-based version of the paradox holds. In addition to theory, our simulation and data results show how all the concepts can be applied to synthetic and real networks. Where applicable, we draw connections to prior work to make this an accessible and comprehensive paper that lets one understand the math behind the Friendship Paradox and its basic extensions. [ABSTRACT FROM AUTHOR]

With better access to e-commerce and its adoption in rural China, farmer entrepreneurship has thrived and released renewed vigor and vitality in rural areas, serving as a reference for global poverty reduction and rural entrepreneurship. This paper probes into the relationship between e-commerce adoption, social networks, and farmer entrepreneurship to find a mechanism of e-commerce to promote farmer entrepreneurship. This research uses household survey data from the Chinese General Social Survey of 2017. The cross-sectional research method with a sample size of 1306 was adopted in this study to establish links between various constructs. This study used secondary data that had already been collected on the subject. In this research, the quantitative secondary data analysis method was applied to numeric data arithmetically analyzed. The findings indicate that, first, e-commerce has significantly boosted farmer entrepreneurship, particularly in less-developed regions, where the effect is slightly more robust (the central and western regions). Second, the size and heterogeneity of social networks partially mediate the relationship between e-commerce adoption and farmers' entrepreneurial decision-making at the state level. In addition, the current data analysis has not verified the logical relationship between e-commerce adoption, the strength of ties, and farmer entrepreneurship. Thirdly, social network size and heterogeneity have a greater mediation effect than the regional subsample study results. This paper adds to the current theoretical approach about farmer entrepreneurship, e-commerce adoption, their relationship with social network size, heterogeneity, and strength of ties based on the Chinese General Social Survey. [ABSTRACT FROM AUTHOR]

List experiments encourage survey respondents to report sensitive opinions they may prefer not to reveal. But, studies sometimes find that respondents admit more readily to sensitive opinions when asked directly. Often this over-reporting is viewed as a design failure, attributable to inattentiveness or other nonstrategic error. This paper conducts an exploratory analysis of such a 'failed' list experiment measuring vote buying in the 2019 Nigerian presidential election. We take this opportunity to explore our assumptions about vote buying. Although vote buying is illegal and stigmatized in many countries, a significant literature links such exchanges to patron-client networks that are imbued with trust, reciprocity and long-standing benefits, which might create incentives for individuals to claim having been offered to participate in vote buying. Submitting our data to a series of tests of design, we find that over-reporting is strategic: respondents intentionally reveal vote buying and it's likely that those who reveal vote buying have in fact being offered to participate in vote buying. Considering reasons for over-reporting such as social desirability and network benefits, and the strategic nature of over-reporting, we suggest that "design failure" is not the only possible conclusion from unexpected list experiment results. With this paper we show that our theoretical assumptions about sensitivity bias affect the conclusions we can draw from a list experiment. [ABSTRACT FROM AUTHOR]

This paper focuses on a new trend in the Chinese labor market: more retirees pursue reemployment, especially for elite older workers. This paper employs a social network approach to explore factors influencing their continued working choices. The study adopts the semi-structured interview method. It involves interviewing 12 reemployed older workers in the education, medical, and skill services sectors in Hebei Province, China. The findings reveal internal and external factors contributing to their reemployment. Internally, the study identifies abundant human capital, positive values, reduced family burdens, and sustaining physical well-being as critical factors influencing reemployment. Externally, supportive social networks, government coordination, the preservation of traditional culture, and suitable work conditions also play pivotal roles in facilitating the reemployment process. Economic incentives are not the primary motivation; instead, ongoing social engagement within social networks and cultural contexts plays a crucial role in influencing their decisions regarding reemployment. [ABSTRACT FROM AUTHOR]

The social network is an important way to promote subjective poverty alleviation. Based on China Labor Dynamics Survey (CLDS) data from 2014, 2016, and 2018, this paper analyzes the action mechanism of social networks on the subjective poverty of Chinese residents. The results find that social network acts on subjective poverty through social support and reference groups. Specifically, social networks provide emotional and instrumental social support for individuals, which can effectively alleviate subjective poverty. In addition, when the social network members serve as the reference group, the higher the status of individuals in the network, the weaker the subjective poverty. Secondly, objective poverty is the mediating path between the social support function of social networks and subjective poverty. Finally, objective poverty is a regulator of the effect of social networks as a reference group on subjective poverty. When individuals are objectively poor, the alleviating effect of social networks as a reference group on subjective poverty is weakened. In summary, placing social networks' social support and reference group functions under the same framework helps fully grasp social networks' social benefits. As a result, consolidating China's poverty reduction achievements and focusing on the informal social insurance role of social networks are conducive to alleviating subjective poverty. [ABSTRACT FROM AUTHOR]

Influence Maximization (IM) is an important topic in the field of social network analysis, and is widely used in viral marketing, recommendation systems, rumor prevention and other fields. The meta-heuristic method has excellent performance because of high scalability and low complexity, however, the objective function of existing meta-heuristic methods can only be applied to propagation models with small probabilities. In addition, there is room for further improvement in performance of meta-heuristic methods. In order to solve the above problems, this paper transforms the influence maximization problem into an optimization problem and designs a novel objective function based on the six degrees of separation theory of social networks. Then, with the designed objective function, this paper discretizes the harris hawks optimization (HHO) algorithm by redefining the energy and location representation rules for influence maximization problem. Experimental results on eight real datasets demonstrate that the proposed objective function exhibits high accuracy and generality, suitable for various probability propagation models. With the exploration and exploitation process in steps, dynamically using different strategies various situations, the proposed DHHO algorithm exhibits better performance in dealing with influence maximization problem, outperforming the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) with Social Networks (SN) has given rise to a new paradigm called Social IoT, which allows users and objects to establish social relationships. Nonetheless, trust issues such as attacks have emerged. These attacks can influence service discovery results. A trust management mechanism has become a major challenge in the Social IoT to prevent these attacks and ensure qualified services. A few studies have addressed trust management issues, especially those that prevent trust attacks in Social IoT environments. However, most studies have been dedicated to detect offline attacks with or without specifying the type of attack performed. These works will not be able to prevent attacks by aborting transactions between users because their primary purpose is to detect an offline attack. In addition, they do not consider security properties. This research paper aims to provide a detailed survey on trust management mechanism to handle trust attacks in Social IoT. In this research paper, we compared the techniques and technologies whose common point is attack prevention and demonstrated that blockchain technology can play a key role in developing a trust management mechanism that can prevent trust attacks while maintaining security properties. Then, we proposed combining the Apache Spark Framework with blockchain technology to provide real-time attack prevention. This combination can assist in creating upgraded trust management mechanisms in Social IoT environments. These mechanisms aim to prevent attacks in real-time through considering the security properties. Lack of survey papers in the area of trust attack prevention in real-time stands for an important motivational factor for writing this paper. The current research paper highlights the potential of the blockchain technology and Apache Spark in terms of developing an upgraded trust management able to prevent trust attacks in real-time.This paper provides a comprehensive survey on trust management mechanisms and approaches to handle trust attacks in Social IoT. Lack of such papers increases the significance of this paper. It also offers potential future research directions in terms of real-time trust attack prevention. [ABSTRACT FROM AUTHOR]

Studies on the symbol and feedback effects on the opinion based on the theory are lacking. Acknowledging that the media express their stance and opinion and that negative opinions are critical to policy change, this paper fills the gap in the literature by illustrating and comparing the effects of emotional and cognitive symbols and positive and negative feedback on the liberal and conservative newspapers' negative opinions of South Korean President Park Geun-hye's administration (Park administration) after the Sewol Ferry sank. This study used qualitative and quantitative methods to analyze the archival data, including 424 newspaper editorials and economic data published from April to December 2014. Multiple regression analyses were conducted following a content analysis of newspaper editorials, and network analysis was used to analyze the data. The results mostly supported the hypotheses that symbols and feedback affect the negative opinion on the political discourse, with new findings that deviate from the existing theories. The emotional symbols exerted a stronger influence on the negative opinion compared to cognitive symbols, regardless of the newspaper's stance. The political system's response to the positive and negative feedback was not definite; instead, it varied depending on the situation and newspaper perspective. The liberal newspaper responded to symbols and feedback more sensitively compared to the conservative one under the conservative administration. The conservative newspaper expressed more lenient negative opinions towards the conservative administration than the liberal newspaper, supporting the home team effect. These findings have practical and theoretical implications for future studies, highlighting the application of opinion networks in social science. [ABSTRACT FROM AUTHOR]

A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images. [ABSTRACT FROM AUTHOR]

Within the realm of social networks, most research efforts have concentrated on identifying crucial nodes in networks, with little attention paid to identifying critical edges. Nevertheless, edges, serving as conduits for information dissemination, hold significant importance. Mining critical edges in networks can serve as a valuable target for both network disassembly as an attack strategy and network preservation as a defensive measure. This paper introduces the k -sup structure by taking into account the strength of relationships between nodes and investigates the critical subgraph based on the k -sup structure. Furthermore, this paper distinguishes between the significance of inter-community edges and intra-community edges, proposing an importance indicator based on the k -sup structure. To validate the effectiveness of the proposed indicator, comparative experiments are conducted with seven classic edge importance indicators on eight real-world network datasets and three synthetic network datasets. The experimental results demonstrate that the proposed indicator assesses their importance, exhibiting superiority over alternative methods in terms of network connectivity. [ABSTRACT FROM AUTHOR]

Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions improves accuracy. This paper proposes a robust method to identify communities based on the improved user interaction degree, the weighted quasi-local structural similarity measure, and the frequent pattern mining on user interactions. In the community creation phase, influential users are identified based on eigenvector centrality and users who interact with them the most are extracted based on frequent pattern mining. In the community expansion phase, we introduce a measure to calculate the degree of user interactions based on the local clustering coefficient improved by interactions between common neighbors. We present two strategies to expand the community. The first strategy, a direct connection, exists between a user outside and a user inside the community. Their similarity is calculated based on the combined measure of improved user interaction degree and user degrees. The second strategy is if two users do not have a direct connection, we consider their communication paths. Therefore, we present a similarity measure combining a quasi-local path-based measure and an improved user interaction degree. Analysis of Higgs Twitter and Flickr datasets using internal density, Normalized Mutual Information, and Adjusted Rand Index shows that this paper's method outperforms the other five community detection methods. Furthermore, our method has more robustness than other relevant methods. [ABSTRACT FROM AUTHOR]

In this review paper we summarize a recent progress on the problem of describing range of dynamics supported by a network. We show that there is natural connection between network models consisting of collections of multivalued monotone boolean functions and ordinary differential equations models. We show how to construct such collections and use them to answer questions about prevalence of cellular phenotypes that correspond to equilibria of network models. [ABSTRACT FROM AUTHOR]

This paper introduces the Better Hide Communities (BHC) benchmark dataset aimed at standardizing evaluations in community deception across networks. BHC addresses the need for a common framework to assess the effectiveness of existing and perspective deception strategies by enabling their comparative analyses. BHC serves as a foundation for future work in developing sophisticated algorithms for community deception, enhancing the understanding of algorithmic abilities to employ deceptive measures within communities. Additionally, it offers valuable insights into the varying degrees of resilience that different detection algorithms exhibit against deception strategies. [ABSTRACT FROM AUTHOR]

Social Networks have been in continuous growing during the last decades. The huge amount of information and applications has led to an increase in the interest of scientists and practitioners in the study of problems related to the influence in Social Networks. Some of the wide variety of real-world applications in this area are viral marketing, disease analysis, rumor detection, public opinion, among others. In this paper, the Adaptive Multi Round Influence Maximization problem is studied, in which the influence of a set of selected users (seed set) is propagated in multiple rounds independently, with the possibility of selecting different seed sets in each round. Therefore, seed sets can be adaptively selected based on the propagation results in the previous rounds. Since each node is activated with a certain probability, the total number of activated nodes must be calculated through an Influence Diffusion Model (IDM), which results in a rather computationally demanding method. In this research, the Independent Cascade Model is considered, which is one of the most extended IDMs, and also the one used in the best previous method. Practitioners highlight the relevance of designing an algorithm capable of efficiently solving the problem. In this research, the problem is addressed by considering the Variable Neighborhood Search methodology, proposing a novel constructive method that relies on independent probability based on events, and an intelligent local search method. Our best algorithm is compared with the state-of-the-art method, named AdaIMM, to analyze the performance of the proposal. The obtained results show the superiority of the proposal in both quality (influence spread) and computing time, obtaining the best solution in all the 40 instances considered requiring half of the computing time than the best previous approach (28 s vs. 53 s). Additionally, the best previous method presents an average deviation of 24.23%. These results are further confirmed by conducting non-parametric statistic tests. [ABSTRACT FROM AUTHOR]

Online digital networks, including social networks, have significantly impacted individuals' personal and professional lives. Aside from exchanging news and topics of interest, digital networks play an essential role in the diffusion of information, which frequently significantly impacts worldwide societies. In this paper, we present a new mathematical epidemic model for digital networks that considers the sentiment of solitary misinformation in the networks and characteristics of human intelligence that play an important role in judging and spreading misinformation inside the networks. Our mathematical analysis has proved the existence and validity of the system in a real-time environment. Considering the real-world data, our simulation predicts how the misinformation could spread among different global communities and when an intervention mechanism should have to be carried out by the policyholders. Our simulation using the model proves that effective intervention mechanisms by isolating the fake news can effectively control the spread of misinformation among larger populations. The model can analyze the emotional and social intelligence of groups frequently subjected to disinformation and disseminating fake news. [ABSTRACT FROM AUTHOR]

Residents' participation is crucial for sustainable old neighborhood regeneration. Residents' intentions to participate in the regeneration are low in China, resulting in unsustainable neighborhood development problems. Therefore, it is essential to investigate the drivers of residents' participation. Residents' behavioral mechanisms are deeply embedded in their social networks, especially in acquaintance social networks typical of old neighborhoods in China. By constructing a mediated moderation model based on the Theory of Planned Behavior (TPB) and danwei system and taking the old neighborhood regeneration in Xi'an as a case study, this paper investigates the paths and mechanisms of the social network embeddedness, TPB, and danwei system on residents' participation intention. The results show that social network embeddedness can improve residents' intention to participate in regeneration through both direct and indirect pathways, and its indirect effect is much larger than the direct effect. Attitude, subjective norm, and perceived behavioral control significantly and positively mediated the contribution of social network embeddedness on participation intention. However, the specific mediating effect of attitude is significantly smaller than the other two. The danwei system has a significant moderating effect in the first stage of the relationship between social network embeddedness and participation intention. These imply that the extrinsic social factor of social network embeddedness is internalized as a driving force for convergence and motivation of residents' participation intentions mainly through the normative pressure and exchange of experiences between acquaintances and that the danwei system reinforces this extrinsic-intrinsic facilitation process through the danwei's "physical presence." These findings provide revelatory countermeasures for exploring the factors that motivate residents to participate actively in neighborhood regeneration and encourage their involvement at the intervention level. [ABSTRACT FROM AUTHOR]

The Ego Network Model (ENM) is a model for the structural organisation of relationships, rooted in evolutionary anthropology, that is found ubiquitously in social contexts. It takes the perspective of a single user (Ego) and organises their contacts (Alters) into a series of (typically 5) concentric circles of decreasing intimacy and increasing size. Alters are sorted based on their tie strength to the Ego, however, this is difficult to measure directly. Traditionally, the interaction frequency has been used as a proxy but this misses the qualitative aspects of connections, such as signs (i.e. polarity), which have been shown to provide extremely useful information. However, the sign of an online social relationship is usually an implicit piece of information, which needs to be estimated by interaction data from Online Social Networks (OSNs), making sign prediction in OSNs a research challenge in and of itself. This work aims to bring the ENM into the signed networks domain by investigating the interplay of signed connections with the ENM. This paper delivers 2 main contributions. Firstly, a new and data-efficient method of signing relationships between individuals using sentiment analysis and, secondly, we provide an in-depth look at the properties of Signed Ego Networks (SENs), using 9 Twitter datasets of various categories of users. We find that negative connections are generally over-represented in the active part of the Ego Networks, suggesting that Twitter greatly over-emphasises negative relationships with respect to "offline" social networks. Further, users who use social networks for professional reasons have an even greater share of negative connections. Despite this, we also found weak signs that less negative users tend to allocate more cognitive effort to individual relationships and thus have smaller ego networks on average. All in all, even though structurally ENMs are known to be similar in both offline and online social networks, our results indicate that relationships on Twitter tend to nurture more negativity than offline contexts. [ABSTRACT FROM AUTHOR]

Purpose: Spinal cord injury (SCI) is impairment of the spinal cord that adversely affects patients' health and quality of life (QoL). The aim of the study was to assess the quality of life (QoL) and related factors in patients with traumatic spinal cord injury in Middle Eastern countries. Methods: PubMed, Ovid Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EBSCOhost, psycINFO, and Google Scholar were systematically searched for eligible studies published in peer-reviewed journals up till October 2023. Results: Out of 1060 papers found in the literature, only 18 studies from Iran, Lebanon, and Turkey met the inclusion criteria. The majority of the studies employed the SF-36 scale to assess for QoL. In general, all SCI patients had reduced QoL scores when compared to the healthy population. Some of the factors such as age, education level, the level of injury, time since injury onset, marriage, and job opportunities were correlated with SCI subjects QoL. Our papers were assessed and found to be of both good and high quality. Conclusion: This review emphasizes the significant shortage of QoL studies among SCI patients in the Middle East countries and highlights the importance of improving the QoL of this marginalized population. This work should enhance the governments to establish rehabilitation centers, social and economic support systems, and mental health services to diminish complications arising from SCI. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of hesitant bipolar-valued fuzzy graph (HBVFG), which captures the two opposing perspectives, namely the positive and negative opinions. The novelty, importance and implications of this concept are illustrated by some results, examples, and graphical representations. There are, respectively, some theoretical terms of graphs such as partial directed hesitant bipolar-valued fuzzy subgraph (HBVFSG), directed HBVFSG, directed spanning HBVFSG, strong directed HBVFG and complete directed HBVFG which are introduced. The operations, such as Cartesian, direct, lexicographical, and strong products, are also defined between two HBVFGs with examples. The mapping relations, such as homomorphism, isomorphism, weak isomorphism, and co-weak isomorphism, are derived with an example. The applications of directed HBVFGs with algorithms for finding the optimal path in a network and the dominant node and influence of index with the self-persistence degree of a node in a social network are presented. For each problem, an algorithm is developed and its effectiveness is demonstrated by examples. The proposed concept is assessed in terms of theory and practice. The benefits of the proposed solution are highlighted and a clear comparison is made with the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a multidimensional social open model to evaluate the teaching strategies adopted during the COVID-19 pandemic by assessing the decisions made by teachers by a group of teachers acting as evaluators. Based on the analysis of previous studies on teaching, this study aims to propose a formal model for the evaluation of teaching strategies in four dimensions: sustainability, usability, accessibility, and creativity. The use of information technologies to measure teaching strategies can bring decisive advantages. This work has been inspired by social rating systems of social networks to propose a measurement system in which a potential large number of evaluators with different levels assess the strategies. In addition, the proposal also includes the evaluation of the evaluators' own work, assigning confidence levels that are based on their experience but also on their evaluations. In this way, we have a social measurement system, in the sense that participation is open to a large number of evaluators. A large community of teacher evaluators will increase the objectivity of the measurement. The outcome of the system will be a characterization of the teaching strategies that will allow to decide in the future which ones should be adopted according to the needs of each one. [ABSTRACT FROM AUTHOR]

In this paper, by using a lifecycle perspective, four stages related to the extraction, refining and processing of copper were identified. The different behaviors of countries in the import/export networks at the four stages synthetically reflect their position in the global network of copper production and consumption. The trade flows of four commodities related to the extraction, refining and processing of copper of 142 nations with population above 2 millions based on the UN Comtrade website (https://comtrade.un.org/data/), in five years from 2017 to 2021, were considered. The observed trade flows in each year have been modelled as a directed multilayer network. Then the countries have been grouped according to their structural equivalence in the international copper flow by using a Multilayer Stochastic Block Model. To put further insight in the obtained community structure of the countries, a deep learning model based on adapting the node2vec to a multilayer setting has been used to embed the countries in an Euclidean plane. To identify groups of nations that play the same role across time, some distances between the parameters obtained in consecutive years were introduced. We observe that 97 countries out of 142 consistently occupy the same position in the copper supply chain throughout the five years, while the other 45 move through different roles in the copper supply chain. [ABSTRACT FROM AUTHOR]

Social networks are the primary source of information sharing, and with the abundant availability of the internet, more and more information is being dumped into social networks from various sources. There is no proper credibility check, so social networks have become the main source for manipulated or inclined news. As information comes in multiple forms, there is a need for multimodal models to detect Fake News. Many features being specific to the domain may mislead the process of classifying news. Obtaining specific features that could retain the framework's capability becomes the point of interest. The paper proposes a novel method first to obtain features from a specific attribute and use a mechanism to derive optimal features that helps in attaining the ability to detect Fake News across domains. Genetic algorithms, termed nature-driven, aim at exploring the best optimal features that enhance the detection capability of the framework. Therefore, the paper proposes a model that works on textual features, metadata, and author embedding data and uses genetic algorithms to extract optimal features. These multimodal are combined using fusion methods, and finally, 2-Layer MLP is used to detect the Fake posts. Fakeddit, a multimodal dataset, is used for analysis. Text features are generated from the Title parameter, which is used as clickbait to attract attention. Genetic algorithms like two-phase gray wolf (TMGWO) and improved sine cosine algorithms (ISCA) are used to extract the best optimal features from the dataset. The proposed model has achieved an accuracy of 91.05% when ISCA is used to extract optimal features, BERT is used to analyze textual features, and metadata and author embedding are combined using Concatenate. The model has shown that extracting optimal features has enhanced the overall accuracy of the multimodal model. The proposed model is observed to outperform other state-of-the-art models working on similar data. To the best of our knowledge, the proposed framework is prominent in obtaining specific features that could help to classify news or articles as real or fake using the blend of Genetic-based and fusion-based models. [ABSTRACT FROM AUTHOR]

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to distinguish by producing vivid news or images. Additionally, modern social networks are becoming flatter and more diverse, making conventional trust management methods unfeasible. There is an urgent need to design a suitable blockchain consensus for decentralized trust management. In this paper, we propose a novel three-layer autonomous trust management model for online social users based on blockchain technology and group decision making. Instead of solving a hash puzzle, which consumes too much computational resources for social network users, this paper proposes a lightweight and verifiable blockchain consensus proof of trust for decentralized trust management. Furthermore, performance analysis and simulation results show that our model is valid and flexible for both synthetic networks and real social networks. [ABSTRACT FROM AUTHOR]

Nowadays, large-scale individual trajectories can be collected by various location-based social network services, which enables us to better understand human mobility patterns. However, the trajectory data usually contain sensitive information of users, raising considerable concerns about the privacy issue. Existing methods for protecting user trajectory data face two major challenges. First, existing methods generally emphasize on data privacy but largely ignore the data utility. Second, most existing work focus on protecting the privacy of users' check-in locations, which is not sufficient to protect against the trajectory-user linking (TUL) attack that infers a user's identity based on her/his trajectories. In this paper, we for the first time propose a collaborative adversarial learning model named BPUCAL to effectively resist the TUL attack and preserve the data utility simultaneously. The general idea is to fool the TUL model by adding a small perturbation on the original trajectory data to balance the data utility and privacy. BPUCAL perturbs a few numbers of carefully identified check-ins of a trajectory which are pivotal for a TUL model to infer the identity of a user. Specifically, BPUCAL contains three parts: a perturbation generator, a discriminator, and a TUL model. The generator aims to produce learnable noise and adds it to the original trajectories for obtaining perturbed trajectories. The perturbed trajectories with a minimal changes compared to the original trajectories can deceive both the discriminator and the TUL model. Extensive experiments are conducted over two real-world datasets. The results show the superior performance of our proposal in balancing data privacy and utility on trajectory data by comparison with baselines. [ABSTRACT FROM AUTHOR]

Conflicts and wars have a profound impact on human needs, as vividly illustrated by the ongoing crisis in Gaza. This paper aims to systematically examine how these crises affect individuals' ability to meet their basic and higher-order needs, as conceptualized by Maslow's hierarchy. By employing Maslow's framework, this paper seeks to uncover the specific ways in which conflict disrupts the fulfillment of essential human needs and to identify targeted interventions that can mitigate these effects. Through this focused lens, I hope to contribute to a deeper understanding that supports efforts to end the war. In this paper, a methodical approach that integrates a review of online news, media reports, published official reports from international organizations, and academic publications was adopted to identify the current challenges in Gaza impacting the different levels of need. An exploration through the framework of Maslow's hierarchy revealed the extensive humanitarian challenges stemming from the blockade and military actions since 2008. There are significant disruptions across all levels of Maslow's hierarchy. Despite the severe compromise in basic needs like safety and physiological sustenance, there is a notable resilience in the pursuit of transcendence needs among the residents by transcending personal suffering through acts of solidarity, altruism, and community engagement and exhibiting a deeper sense of spirituality and faith that provide solace and a sense of connection to a larger purpose. There is an urgent need to end the conflict, facilitate unrestricted aid, and address the multifaceted needs of Gaza's population. Strategies that support both immediate survival and long-term personal and communal growth in the face of adversity are critically needed. Essential initiatives include establishing community support networks for critical needs like food, shelter, and healthcare, alongside promoting economic stability through livelihood projects. Additionally, the development of online education programs offering vocational training and digital literacy, in partnership with international institutions, is crucial. These initiatives, tailored to Gaza's context and aimed at overcoming siege-related barriers, are vital for empowering residents with the skills for community rebuilding, economic self-sufficiency, and enhanced social cohesion. Such strategies are indispensable for addressing immediate adversities while laying a foundation for sustainable future growth. [ABSTRACT FROM AUTHOR]

This paper shows how information about the network's community structure can be used to define node features with high predictive power for classification tasks. To do so, we define a family of community-aware node features and investigate their properties. Those features are designed to ensure that they can be efficiently computed even for large graphs. We show that community-aware node features contain information that cannot be completely recovered by classical node features or node embeddings (both classical and structural) and bring value in node classification tasks. This is verified for various classification tasks on synthetic and real-life networks. [ABSTRACT FROM AUTHOR]

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles and was able to successfully identify the political leniency of the user profiles with 99% confidence level, and also on a synthetic dataset with 2500 tweets, where the predicted accuracy and F1 score were 0.99 and 0.985 respectively, and 0.97 and 0.975 when neutral users were also considered for classification. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in the number of users belonging to the different clusters. [ABSTRACT FROM AUTHOR]

Based on fieldwork in Fuzhou's Changle county―an emigrant-sending region in the People's Republic of China (PRC)―this paper analyzes transnational judicial practice as it pertains to resolution of transnational migrants' legal disputes. Resolution of "foreign-related" disputes had been obstructed by spatial and temporal barriers and difficulties navigating the PRC's judicial system. However, a more efficient path for transnational migrants to resolve their legal disputes emerged due to: the PRC government's increased concern toward transnational migrants; implementation of new governance procedures within the judicial system; utilization of the transnational migrant's informal social networks; and, application of internet-enabled communication technologies. By examining the extra-territorial reach of state authority in regulating transnational activities, analysis from the 26 interviews conducted reveals the key role played by leaders in Overseas Chinese Associations, who act as informal judicial "mediators" between the PRC's judicial system and transnational migrants. Additionally, internet-enabled communication technologies and social networks have helped collapse the physical and temporal distances that previously separated transnational migrants from their country of origin. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the development of an integrated patient-centred cardiac care registry spanning the initial 5 years (September 2017 to December 2022). The Netherlands Heart Registration facilitates registration committees in which mandated cardiologists and cardiothoracic surgeons structurally evaluate quality of care using real-world data. With consistent attendance rates exceeding 60%, a valuable network is supported. Over time, the completeness level of the registry has increased. Presently, four out of six quality registries show over 95% completeness in variables that are part of the quality policies of cardiology and cardiothoracic surgery societies. Notably, 93% of the centres voluntarily report outcomes related to open heart surgery and (trans)catheter interventions publicly. Moreover, outcomes after implantable cardioverter-defibrillator and pacemaker procedures are transparently reported by 26 centres. Multiple innovation projects have been initiated by the committees, signalling a shift from publishing outcomes transparently to collaborative efforts in sharing healthcare processes and investigating improvement initiatives. The next steps will focus on the entire pathway of cardiac care for a specific medical condition instead of focusing solely on the outcomes of the procedures. This redirection of focus to a comprehensive assessment of the patient pathway in cardiac care ultimately aims to optimise outcomes for all patients. [ABSTRACT FROM AUTHOR]

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn. [ABSTRACT FROM AUTHOR]

Abstract This research analyses collaboration among members of an insurance supply chains network based on a social network analysis (SNA) approach. A case study strategy is employed, and data are collected from the firm's accounts in an enterprise online social network via the Yammer® platform. Several SNA techniques (cohesion analysis, multidimensional scaling analysis, cluster analysis) are applied. Firstly, this paper emphasises who the most involved suppliers are and with whom they collaborate. Secondly, it confirms that collaboration appears both within and between supply chain levels. Thirdly, it highlights firms' tendency to focus on the external environment. Suppliers are interested in not only collaborating with the focal firm, but also with other suppliers situated at the same or a different level. This work has implications for academics and practitioners as it presents a new approach to evaluate and improve collaboration among the members of a multilevel supply chains network. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) have become increasingly popular on the web in recent years. There are millions of users on these networks, and they generate a great deal of interaction among them. Network sampling is often used to study and measure OSNs, which produce a small, accessible network with a limited number of edges and nodes. In this paper, two algorithms are proposed to sample OSNs. The first algorithm finds several spanning trees with the highest number of nodes that satisfy a given degree constraint from randomly chosen root nodes; edges and nodes of computed spanning trees are ranked based on how often they appear in them. Finally, a fraction of the highly ranked nodes and edges are considered in the sampled network. Second, a partial spanning tree algorithm is proposed in place of a full spanning tree algorithm to improve the performance of the first algorithm. We conduct several experiments on well-known real networks to determine the performance of the proposed sampling algorithms. As a result of the experiments, the proposed algorithms outperformed some of the baseline and recently presented algorithms in terms of the Kolmogorov-Smirnov (KS) test, Skew Divergence (SD) distance, and Normalized Distance (ND). [ABSTRACT FROM AUTHOR]

Multilayer social networks are the main representative form for today's social networks. In fact, the multiplicity of relations, the huge amount of data and the dynamic nature of nowadays social networks impose the representation of the network with multiple layers. This new representation makes network analysis more challenging especially Community retrieval. So, researchers propose different approaches to handle these challenges to detect accurate communities in the multilayer networks. The main goal of this paper is to present a novel and comprehensive view on community detection strategies within multilayer social networks. To do so, we provide a taxonomy of existing methods in static and dynamic multilayer social networks. Additionally, we introduce a "four worlds framework" to offer a comprehensive comparison of the different existing community detection methods. Lastly, we outline potential avenues for future research and highlight some unresolved challenges in this domain. [ABSTRACT FROM AUTHOR]

Social network platform facilitates a potential environment in which huge amounts of information are derived from Social Networks (SNs) for attaining significant social targeting. Illegitimate disclosure of users' private data can introduce damaging consequences and may even cause threat to life of users. Majority of the existing privacy preserving research works only handle the issues of privacy but fails in concentrating on the process of facilitating services to normal SN users that includes data access, retrieval and sharing services. Guaranteeing sensitive data security and provisioning efficient social network services to users with privacy is a herculean task. In this paper, Ring Signature-based Blockchain Mechanism (RSBM) is proposed for guaranteeing privacy preservation in OSNs. RSBM is proposed as a reliable methodology for achieving SN services and securing sensitive data by integrating the merits of public-key cryptography technique and blockchain. It is proposed with the capability of preventing potential damage to users' interested data. RSBM is specifically proposed as a privacy preserving protocol that derives the characteristics of elliptical Curve-Inspired Ring Signature (CIRS) for ensuring complete anonymity for user identity. It specifically used consortium blockchain for recording and tracking the transactions that are carried by the users of the OSNs with maximized decentralization and reliability. The security analysis of the proposed RSBM confirms improved resistance to anonymity attacks that could be possibly launched over OSNs. It is also identified to include better advantage in concealing user identity and securing sensitive data. [ABSTRACT FROM AUTHOR]

While our understanding of long-term trends in material wealth inequality in prehistoric societies has expanded in recent decades, we know little about long-term trends in other dimensions of wealth and about social developments within particular societal segments. This paper provides the first evidence of inequality in relational wealth within the upper societal segment of a supra-regional network of communities in prehistoric Central Europe over the first four millennia BCE. To this end, we compiled a novel dataset of 5000 single-funeral burial mounds and employed burial mound volume as a proxy for the buried individual's relational wealth. Our analysis reveals a consistently high level of inequality among the buried individuals, showing a wave-like pattern with an increasing trend over time. Additionally, our findings show temporal shifts in the size of the upper societal segment. Based on a review of archeological and paleo-environmental evidence, the temporal change in inequality may be explained by technological progress, climate and population dynamics, trade and social networks, and/or sociopolitical transformations. [ABSTRACT FROM AUTHOR]

The Influence Maximization problem (IMP) is a fundamental algorithmic challenge that involves selecting a set of k users, known as the seed set, from a social media network to maximize the expected number of influenced users or influence spread. In recent years, Influence maximization has been extensively studied due to its numerous applications in marketing and other domains. However, many existing algorithms neglect the impact of communities on influence maximization, and some other methods suffer from scalability issues and time-consuming computations. In this paper, we propose a fast, and scalable algorithm called Community based Influential maximization (CB-IM) to address these limitations. This method leverages the community structure of the network to select k users, thus maximizing the influence spread. The CB-IM algorithm comprises two main components for influence maximization: (1) seed selection and (2) local community spreading. In the seed selection phase, we extract seed nodes from communities identified through a community detection algorithm. To effectively reduce computational complexity and distribute seed nodes across communities, we carefully select meaningful communities. The second phase involves the propagation of influence within independent communities. In this step, the final seed nodes are strategically distributed to facilitate local spreading through simple paths within the communities. To evaluate the performance of proposed method, we conducted a series of experiments using real networks. The proposed CB-IM algorithm demonstrated superior performance over other algorithms in terms of influence spread and running time, highlighting its effectiveness. [ABSTRACT FROM AUTHOR]

The present investigation is part of the Social Networks and Agency Project (SNAP), an 18-month longitudinal mixed methods study following adolescents located in New South Wales, Australia. The SNAP study aims to understand how online and offline social networks impact the development of sexual agency over time. This paper presents an analysis of the second interview time point in the SNAP study. Fifty adolescents (aged 14–17) completed a semi-structured interview either face-to-face or via Skype about their romantic relationships, including where they get their sexual health information and how they flirt (both online and offline). Interviews were transcribed and analyzed using a grounded theory approach. The core theme was: Young people clearly demonstrate emerging sexual agency. The three characterizing themes were: (1) Young people have complex schemas about flirting, dating, and entering romantic and sexual relationships; (2) Young people have high literacy in social media, traditional media and pornography; and (3) Peers influence most aspects of developing sexual agency, including sex and relationship attitudes. We discuss possible methods to utilize adolescents' social networks in facilitating the development of sexual agency. [ABSTRACT FROM AUTHOR]

While a rich literature investigates how and why NPOs use social media, research on why they differ in their social media adoption (SMA) is limited. In this paper we examine how NPOs' interorganizational partner portfolio characteristics can enable or constrain their adoption of social media, including blogs and videos, conventional social media (Facebook, Twitter...) and crowd-based platforms (crowdfunding and petitions). Based on a survey distributed to a sample of environmental NPOs in France, results indicate that NPOs having open networks, whose partners are physically distant, and that have more cross-sectoral partners have higher SMA. Network portfolio management can thus make up for a shortage of financial resources to invest in social media. [ABSTRACT FROM AUTHOR]

Mental illnesses are the leading cause of disease burden among children and young people (CYP) globally. Low- and middle-income countries (LMIC) are disproportionately affected. Enhancing mental health literacy (MHL) is one way to combat low levels of help-seeking and effective treatment receipt. We aimed to synthesis evidence about knowledge, beliefs and attitudes of CYP in LMICs about mental illnesses, their treatments and outcomes, evaluating factors that can enhance or impede help-seeking to inform context-specific and developmentally appropriate understandings of MHL. Eight bibliographic databases were searched from inception to July 2020: PsycInfo, EMBASE, Medline (OVID), Scopus, ASSIA (ProQuest), SSCI, SCI (Web of Science) CINAHL PLUS, Social Sciences full text (EBSCO). 58 papers (41 quantitative, 13 qualitative, 4 mixed methods) representing 52 separate studies comprising 36,429 participants with a mean age of 15.3 [10.4–17.4], were appraised and synthesized using narrative synthesis methods. Low levels of recognition and knowledge about mental health problems and illnesses, pervasive levels of stigma and low confidence in professional healthcare services, even when considered a valid treatment option were dominant themes. CYP cited the value of traditional healers and social networks for seeking help. Several important areas were under-researched including the link between specific stigma types and active help-seeking and research is needed to understand more fully the interplay between knowledge, beliefs and attitudes across varied cultural settings. Greater exploration of social networks and the value of collaboration with traditional healers is consistent with promising, yet understudied, areas of community-based MHL interventions combining education and social contact. [ABSTRACT FROM AUTHOR]

Depending on the actions and reactions in social networks, the opinions of users may have an impact on each other. Nodes who can impact others' opinions are defined as opinion leaders. In this paper, we formulate Opinion Leader Selection (OLS) as an optimization problem, for this purpose, African vultures optimization algorithm (AVOA) and Hunger games search (HGS) algorithms are utilized to choose opinion leaders and optimize the results. The proposed method uses the topological characteristics of the network and the combination of relationships between users, also a model that includes four activities, stability, novelty, and popularity features to calculate the fitness function and other required parameters. The Social Network Analysis (SNA) parameters, SIR, and OSim models are used to investigate the proposed method. Based on the findings of the evaluation, the proposed method provides for the Bitcoin alpha dataset (0.88, 0.7, 0.97, 0.81) percent and the Bitcoin OTC dataset (0.76%, 0.64, 0.96, 0.77) percent for accuracy, recall, precision, and F1-score respectively. Also, on the SIR and OSim models analysis, compared to the other 8 approaches, the proposed method gives a better-spreading ability interval and results. [ABSTRACT FROM AUTHOR]

The approximate uniform sampling of graph realizations with a given degree sequence is an everyday task in several social science, computer science, engineering etc. projects. One approach is using Markov chains. The best available current result about the well-studied switch Markov chain is that it is rapidly mixing on P-stable degree sequences (see DOI:10.1016/j.ejc.2021.103421). The switch Markov chain does not change any degree sequence. However, there are cases where degree intervals are specified rather than a single degree sequence. (A natural scenario where this problem arises is in hypothesis testing on social networks that are only partially observed.) Rechner, Strowick, and Müller–Hannemann introduced in 2018 the notion of degree interval Markov chain which uses three (separately well studied) local operations (switch, hinge-flip and toggle), and employing on degree sequence realizations where any two sequences under scrutiny have very small coordinate-wise distance. Recently, Amanatidis and Kleer published a beautiful paper (DOI:10.4230/LIPIcs.STACS.2023.7), showing that the degree interval Markov chain is rapidly mixing if the sequences are coming from a system of very thin intervals which are centered not far from a regular degree sequence. In this paper, we substantially extend their result, showing that the degree interval Markov chain is rapidly mixing if the intervals are centered at P-stable degree sequences. [ABSTRACT FROM AUTHOR]