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Mark Solms' revision of the concept of drive represents the latest in a series of his indepth reviews of the most fundamental Freudian concepts. The movement of neuropsychoanalysis begun in 2000 with the first Congress focused precisely on a neuroscientific definition of affects and emotions. In 2013, Solms proposed a substantial and profound modification of Freud's structural model, motivating his theses which argue that the instinctual id can only be conscious. In the current paper, Solms (2021a) reaffirms and re-evaluates the centrality of drive theory in psychoanalysis and neuroscience as a starting point for understanding conscious and unconscious phenomena, in normality as well in pathology. [ABSTRACT FROM AUTHOR]

This paper describes the research published during the period 2011-2020 by five leading Spanish peer-reviewed journals of communication, aiming to identify the most common theoretical frameworks and standard theories. A statistically representative sample of the articles published during that period was systematically selected from the five journals of communication ranked as having the greatest continuity and impact according to SJR-Scopus: Profesional de la Información, Comunicar, Communication & Society, Estudios sobre el Mensaje Periodístico and Revista Latina de Comunicación Social. A coding protocol that includes 13 variables and 74 categories was defined and applied to compare the type of theoretical frameworks and the theories in the different subject areas observed. The results show that although almost all the papers studied have a theoretical framework, fewer than half are grounded on recognizable theories. Specifically, journalism and digital communication are the two subject areas whose articles frequently include standard theories: in journalism by the support of classical theories of communication (framing, agenda setting, journalistic professional cultures and roles), and in digital communication by the support of more theoretically dispersed frameworks. Theoretical frameworks developed by literature review are common in other subject areas (such as audiovisual studies, public relations and documentation). [ABSTRACT FROM AUTHOR]

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications. [ABSTRACT FROM AUTHOR]

The Panama Papers comprise one of the most recent influential leaks containing detailed information on intermediary companies (such as law firms), offshore entities and company officers, and serve as a valuable source of insight into the operations of (approximately) 214,000 shell companies incorporated in tax havens around the globe over the past half century. Entities and relations in the papers can be used to construct a network that permits, in principle, a systematic and scientific study at scale using techniques developed in the computational social science and network science communities. In this paper, we propose such a study by attempting to quantify and profile the importance of entities. In particular, our research explores whether intermediaries are significantly more influential than offshore entities, and whether different centrality measures lead to varying, or even incompatible, conclusions. Some findings yield conclusions that resemble Simpson's paradox. We also explore the role that jurisdictions play in determining entity importance. [ABSTRACT FROM AUTHOR]

Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China's multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China's banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China's multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks' transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions. [ABSTRACT FROM AUTHOR]

Based on the textile trade data of 66 countries along the Belt and Road from 2001 to 2020, this paper uses the social network analysis method to measure the network centrality, uses the block model method to investigate the competitive and complementary relationship of textile trade among countries along the Belt and Road, and uses the QAP method to analyze the structure of textile trade network and its influencing factors. The results show that since the implementation of the "Belt and Road" Initiative, the density of textile trade network has increased significantly, and the network presents an obvious core-edge structure. China has always been in the center of textile trade network. Before and after the "Belt and Road" Initiative is put forward, core countries such as China, India and Thailand changed from internal sector to two-way overflow sector, and textile trade competition existed among core countries. The influencing factors on the textile trade network of countries along the Belt and Road are, in descending order, economic scale, regional trade agreements between countries, population scale, cultural proximity and geographical distance. Based on the research conclusions and combined with the textile trade network pattern of the Belt and Road countries, this paper puts forward relevant policy suggestions to promote high-quality development of textile trade between China and "The Belt and Road" countries from the perspective of social network. [ABSTRACT FROM AUTHOR]

Swift identification of a set of influential load nodes within power systems is an important task for effective modern power grid operation and security. This paper suggests a suitable approach of Node Strength (NS) or Bus Strength (BS) scheme for effective and swift identification of sets of influential load nodes within power systems. This effective algorithm relies on the Network Structural Characteristics Theory (NSCT) of power systems. The NS scheme uses circuit theory laws to present the linear relationship among the network structure, the voltage and current in the network. The traditional Complex Network Theory (CNT) or Centrality-Based (Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC), PageRank Centrality (PRC) and Eigenvector Centrality (EC)) methods for identifying influential load nodes within power systems are also presented in the paper. Thereafter, the numerical values of the NS and each of the CNT-based methods associated with the load nodes, are determined. The relative importance of each load node is then ascertained through ranking (in the order of magnitude or priority) of the values obtained for NS and each of the CNT-based methods. Based on the order of priority, the sets of influential load nodes in the network are identified. Furthermore, performance analyses, using statistical correlation coefficient as well as structural risk assessment, are used to establish the best method suitable for quick identification of a set of influential load nodes in the system. The results obtained are corroborated with the network robustness assessment to further validate the proposed approach. The study uses IEEE 5-bus, IEEE 14-bus and Nigerian 28-bus networks as case studies. The comparison of results obtained show that the NS or BS scheme suggested is more suitable for quick identification of sets of influential load nodes in a relatively large-sized power network. [ABSTRACT FROM AUTHOR]

Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews. [ABSTRACT FROM AUTHOR]

The study analyses 359 scientific articles published between 1989 and 2015, based on several criteria. During the analysis, we examine how the number of publications changed over the years and which countries and journals dealt with the chosen topic. In respect to the relationship between authors of publications, we seek to establish how the authors relate to each other by analysing if they more typically publish works on their own, in small or large teams, and if the given area has leading authors. [ABSTRACT FROM AUTHOR]

This paper aims to take a closer critical look at the multiple and multi-layered nature of urban centrality. Centrality is conceptualized here as a kind of prominence, perceived, planned and represented quality within the urban timespace. We employ three distinct ontological categories of the urban centre (centre-as-event, centre-as-thing and centre-as-structure) to take a deeper insight into the symbolism, ideological narratives and planning practices behind the genesis of the prominent urban places and times. With this approach, we expose even the seemingly subtle phenomena that (co-)shape multiple urban centralities. We are empirically focusing on the case of the city of Brno (Czech Republic). Attention is paid in particular to the period of socialism, more specifically to the influence of socialist ideology on the reorganization of urban central places and times. We are trying to overcome the traditional view of centralized and all-encompassing socialist transformation. Instead, the socialist Brno provides the case study to demonstrate a subtle fabric of overlapping, competing or simply coexisting socialist and pre-socialist centralities. We argue that the physical re-centralization of the city was in the end less significant than the efforts to symbolically recode the urban environment. [ABSTRACT FROM AUTHOR]

‘Landscape’ is relatively underexplored in legal scholarship, notwithstanding its occasional centrality to legal analysis, and the ways in which law contributes to the shaping of landscape.Wind energy is an especially rich area for the exploration of landscape; not only do wind farms consistently raise concerns about landscape, but the existence of climate change complicates the response to local concern. Most of the legal literature on ‘knowledge’ focuses on the ways in which different ‘expert’ knowledges find their way into, and then shape, legal processes and decisions. This article is more concerned with the ways in which the planning system receives different knowledge claims, and validates some of them. The discussion turns around four tentative categories of knowledge claim: expert or technical knowledge claims; lay (or sometimes local) knowledge claims; prior institutional knowledge claims; and professional planning knowledge claims. Knowledge of the complex, dynamic and plural idea of landscape in any particular case is constructed in the process of decision making and reason giving through a careful layering of these different sorts of knowledge claim. [ABSTRACT FROM AUTHOR]

This paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool's output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model's attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences. [ABSTRACT FROM AUTHOR]

This paper investigates publication strategies of the most distinguished local government researchers in eight European countries. Drawing upon dependency theory, accumulative advantage theory and ‘utility maximizing’ theory, we compare publication strategies of scholars from countries that vary in terms of the distance from the core of academic knowledge production, also taking into consideration their career length (academic age). The two publication strategies compared are international visibility and domestic visibility. The analysis confirmed the hypothesis that younger scholars are more prone to adopt internationalization strategy which has been institutionally incentivized by NPM reforms. However, the difference among core, catching‐up and peripheral countries is not as sizeable as expected. [ABSTRACT FROM AUTHOR]

Recently, pharmaceutical companies have been striving to occupy a greater percentage of the global market with clinical research actively being conducted through mutual collaboration networks. In particular, the international Botulinum toxin (BT) market is characterized by intensifying competition. The majority of BT research has been conducted in the United States and Europe, whilst various countries have started their regional research through international collaboration networks. With this trend, this study aimed to examine the current collaboration trend and network of clinical trial research in the field of BT by employing both bibliometric and collaboration network analyses with one of the global academic and research databases. Based on the collaboration network, which is organized by 8,296 articles and 10,298 institutes, four centrality measures successfully present several international and regional clinical BT research leading institutes. Moreover, there are some hidden active institutes in this area, which are not highlighted in this paper's bibliometric analysis. This study contributes to improving the understanding of clinical trials in BTresearch, a relevant medical topic. Moreover, collaboration network analysis is one of the most valuable approaches of examining the international and regional clinical research. [ABSTRACT FROM AUTHOR]

Large-scale scientific facility is a complex giant system, which plays an important role in promoting national scientific and technological innovation. The development of large-scale scientific facilities has formed a research cooperation network composed of scientific research institutes, universities, enterprises, and other organizations. The large-scale scientific facilities in China have made significant progress and produced a large number of research achievements and papers. The amount of data available meets the requirements for network analysis. In this study, we first use the research paper data of large-scale scientific facilities from the CNKI database as a sample to obtain data on research collaboration in these facilities. Then, through data processing and using network science theories and modelling tools such as UCINET and Gephi, we construct a research collaboration network of large-scale scientific facilities, with organizational institutions as network nodes and collaboration relationships as edges. Next, we use centrality measures such as degree centrality, betweenness centrality, and closeness centrality to reveal the central positions of different organizational structures in the collaboration network. We also use metrics such as network diameter, network density, average path length, clustering coefficient, as well as the core-periphery model and structural hole measures to explain the cooperation characteristics among organizations and the overall properties of the research collaboration network in large-scale scientific facilities. This study found that due to the addition of new organizations, the density of the research collaboration network in large-scale scientific facilities has been diluted, making the network overall sparse while exhibiting small-world and scale-free characteristics. There are closely connected leadership-oriented small groups within the network, forming tightly-knit clusters of cooperation, with a few core organizational structures holding high positions in the network. The network exhibits a typical core-periphery structure: there are fewer organizations in the core area, while the semi-periphery area is more numerous or roughly the same as the periphery area. Higher education institutions, as important organizations in research activities, generally have lower centrality rankings in the research collaboration network and are not located in structural holes. The leading units play the role of both dominators and bridgers in the network, having absolute control over the network and leading the research process. However, the current network has poor resource circulation. To promote the deep integration and development of the research collaboration network in large-scale scientific facilities, as the initiators and leaders of large-scale scientific facility development, the leading units should promote network communication from multiple perspectives. Research organizations should play a bridging and service role, establish industry-academia-research platforms, enhance network connectivity, engage in high-frequency collaboration, break the communication barriers of existing small groups, strengthen network information communication and knowledge exchange capabilities, and enhance the centrality of university nodes. By leveraging the strong innovation capabilities of universities, the hierarchical structure of the network can be broken, thereby enhancing the innovation capacity of large-scale scientific facilities. [ABSTRACT FROM AUTHOR]

The paper considers a game theory approach to calculating the centrality value of the vertices in a directed graph, based on the number of vertex occurrences in fixed length paths. It is proposed to define vertex centrality as a solution of a cooperative game, where the characteristic function is given as the number of simple paths of fixed length in subgraphs corresponding to coalitions. The concept of integral centrality is introduced as the value of a definite integral of the payoff function. It is shown that this centrality measure satisfies the Boldi–Vigna axioms. [ABSTRACT FROM AUTHOR]

Centrality is a numerical measure that characterizes the structural properties of a graph. In the presented work, centrality is used to analyze the load of the graph of urban roads in the city of Petrozavodsk. In the paper, we describe the method used to construct the road graph, present a modified centrality measure that takes into account the features of the transport network and the distribution of passenger traffic, and demonstrate the results of numerical simulations. For the transport graph, betweenness centralities were calculated with and without regard to the distribution of passenger traffic; a connectivity analysis was performed to identify critical, overloaded and reserve roads, and the routes that make the greatest contribution to the centrality of the most loaded roads. The results show that centrality can be used for the analysis of the structural features of the graph of urban roads, modeling sustainability and planning the development of the transport network. [ABSTRACT FROM AUTHOR]

An H -sequence of a vertex is defined by iteratively applying H -index to neighbors of the vertex in a graph. It is still not known whether changing the initial values affects the convergence of H -sequences. Our purpose is to investigate the properties of H -sequences initialized with arbitrary positive integers. This paper presents the necessary and sufficient conditions for the convergence of H -sequences. Theoretical results reveal the differences between divergent H -sequences of infinite graphs and finite graphs. Divergent H -sequences of infinite graphs are either periodic or non-periodic, whereas divergent H -sequences of finite graphs must be periodic with a period of 2. Based on H -sequences, we define 2-step coreness on the second-order neighbors of a vertex. A new centrality is proposed by combining classical coreness and 2-step coreness. The new centrality captures a wider range of information in a network. Experimental results show that the proposed centrality makes significant improvements over centralities such as betweenness, H -index and coreness. [ABSTRACT FROM AUTHOR]

The Belt and Road has developed rapidly in recent years. Constructing a comprehensive traffic network is conducive to promoting the development of the the Belt and Road. To optimize the layout of the Belt and Road comprehensive traffic network, this paper identifies important cities. First, a weighted super adjacency matrix is defined, which includes sea, air, railway transportation and trans-shipment transportation between these transportation modes. With this matrix, the Belt and Road comprehensive traffic network (B&RCTN) is constructed. To identify important node cities, this paper proposes a method to calculate multi-layer centrality which considers inter-layer relationships. With the results of the above four centrality indexes, the Entropy Weight TOPSIS is used to synthesize the evaluation of the four indexes. Finally, the multi-layer comprehensive centrality rank of node cities is obtained. Result shows that there are 72 important cities in B&RCTN. These important cities are mainly distributed in the east and west of Eurasia. Eastern cities are located in East Asia and Southeast Asia, including 36 cities such as Singapore, Shanghai, Guangzhou, Shenzhen and Hong Kong. Western cities are concentrated in West Asia, Western Europe and North Africa along the Mediterranean coast, including 31 cities such as Istanbul, Dubai, Vienna, Trieste and Koper. There are few important cities in central Eurasia, except Almaty in Central Asia and Colombo in South Asia. In addition, important cities also include Moscow in Eastern Europe, Lagos and Lome in West Africa. Finally, based on the distribution of important cities, this paper puts forward some suggestions on the development of the Belt and Road comprehensive transportation. [ABSTRACT FROM AUTHOR]

Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality—to study causal models of other neurotypical and pathological functional networks—are discussed, and some future lines of research are outlined. [ABSTRACT FROM AUTHOR]

A century of archaeology has identified hundreds of potential sites north of the Corinthian Isthmus on the Greek mainland and in the northeast Aegean contemporary with the Middle Helladic-Late Helladic I Period (MH-LH I, ca. 2100-1550 BC). This paper presents an exploratory nearest neighbor network model to examine connectivity in a scenario where maritime movement included widespread access to the sail (known from the wider Aegean). Analysis of clustering and centrality in the modeled network shows that nodes along the Euboean Gulf were the most important for overall connectivity, and that seemingly geographically isolated sites in the Sporades archipelago and Chalkidiki were crucial for integrating the northern Aegean with central Greece. The later Late Bronze Age palatial centers of Thebes, Orchomenos, and Dimini/Volos on the other hand do not score highly on any centrality measure in this scenario, suggesting that their inherent position on the modeled network did not greatly contribute to their eventual success. This observation is in line with recent scholarship, increasingly pointing to an important endogenous aspect to emerging complexity in central Greece. [ABSTRACT FROM AUTHOR]

The vehicular named data networking (VNDN) is gaining more attention as a future vehicular networking (VN) model. The VNDN's fundamental principles of data naming, data-centric forwarding, and in-network caching make it inherently capable of mitigating the challenges in traditional VN. However, broadcast communication for content discovery and delivery causes VNDN to face challenges such as overhead due to packets redundancy, collision, and retransmission. In this paper, we introduce an Interest-Data-flow tracking-based forwarding scheme (IDTracS) for VNDN. IDTracS aims to improve VNDN performance by prioritizing the potential best forwarders and addressing Interest and Data packet broadcast issues. IDTracS is a receiver- and time-contention-based forwarding scheme that controls both Interest and Data packets forwarding. IDTracS uses the node's degree of centrality to the Interest/Data flow and the node's geo-distance closeness from the last Interest/Data forwarder for the requested content name-prefix. Accordingly, it computes an Interest/Data forwarding delay timer for prioritizing Interest/Data among nodes. The simulation findings reveal that the proposed IDTracS achieves an enhanced Interest satisfaction ratio, delivery delay and network transmission overhead compared to related geo-position- and distance-based protocols. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to measure the nodes' centrality in a multilayer network. The multilayer network represents nodes with different relations between them. The nodes have an initial relevance or importance value. Then, the node's centrality is obtained according to this relevance along with its relationship to other nodes. Many methods have been proposed to obtain the node's centrality by analyzing the network as a whole. In this paper, we present a new method to obtain the centrality in which, in the first stage, every layer would be able to define the importance of every node in the multilayer network. In the next stage, we would integrate the importance given by each layer to each node. As a result, the node that is perceived with a high level of importance for all of its layers, and the neighborhood with the highest importance, obtains the highest centrality score. This score has been named the corner centrality. As an example of how the new measure works, suppose we have a multilayer network with different layers, one per research area, and the nodes are authors belonging to an area. The initial importance of the nodes (authors) could be their h-index. A paper published by different authors generates a link between them in the network. The authors can be in the same research area (layer) or different areas (different layers). Suppose we want to obtain the centrality measure of the authors (nodes) in a concrete area (target layer). In the first stage, every layer (area) receives the importance of every node in the target layer. Additionally, in the second stage, the relative importance given for every layer to every node is integrated with the importance of every node in its neighborhood in the target layer. This process can be repeated with every layer in the multilayer network. The method proposed has been tested with different configurations of multilayer networks, with excellent results. Moreover, the proposed algorithm is very efficient regarding computational time and memory requirements. [ABSTRACT FROM AUTHOR]

Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators in various papers. This paper describes a MATLAB toolbox for computing such centrality indices using efficient numerical algorithms based on the connection between the Lanczos method and Gauss-type quadrature rules. [ABSTRACT FROM AUTHOR]

• Syncretic K-shell is developed to identify the node importance of urban rail transit network. • The invulnerability of urban rail transit network is evaluated. • Cumulative core value is proposed considering both global and local centrality of nodes. • Syncretic core value is the node importance identification criterion of the syncretic K-shell. • Relative size of maximal connected subgraph and relative network efficiency are applied to evaluate the invulnerability performance. In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes. [ABSTRACT FROM AUTHOR]

Due to the advent of IoT and the increasing interest of billions of Internet users towards video contents, a huge multimedia flows has been generated, and as a consequence, a massive load is applied on the underlying core network. This change can affect the network stability and lead to potential performance degradation (such as congestion and delays). This is because multimedia flows are hungry in bandwidth, and also the classical routing protocols currently used in IoT core network (like OSPF) are not adapted yet to support the heavy and the large scale of multimedia traffics with a good quality of service (QoS). In this paper, we introduce the multicommodity-based routing by proposing two contributions, Maximum Concurrent Flow Protocol (MCFPr) and Cache-based Maximum Concurrent Flow (C-MCF). They are conceived based on the Maximum Concurrent Flow approach in order to optimize the routing of multimedia data in the backbone of IoT networks. Both contributions are studied and compared with the state-of-art approaches under different scenarios showing good results, especially in the number of data packets sent (improvement by 50%), and in the transmission time (50% faster compared to the majority), which makes them promising solutions for a rapid and efficient routing in IoT core networks. [ABSTRACT FROM AUTHOR]

Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases. [ABSTRACT FROM AUTHOR]

In recent years, Southeast Asian countries have continued to increase weapons and equipment procurement. It is of great significance to deeply analyze the spatio-temporal characteristics of the pattern of Southeast Asian arms trade. From the regional and national levels, import value, import types, arms export scope, export value and export advantage space, combined with the social network analysis methods such as centrality and community division, this paper analyzed the evolution of the geographical pattern of the arms trade in Southeast Asia from 2001 to 2020. The results show that: (1) The diversity of arms import channels in Southeast Asia is gradually increasing, the value of arms imports is growing rapidly, and Southeast Asia has be‐ come a new hot spot of global arms trade; (2) There are obvious spatial differences in arms imports in Southeast Asia. The arms import values of various countries are distributed in echelons. The types of arms imports are affected by the land-sea distribution of the territory. Sea and air weapons are the focus of weapons procurement in Southeast Asia; (3) In addition to China and South Korea, Southeast Asia's arms market is still monopolized by Russia, the US and other Western developed countries, whose arms export market scope is gradually expanding; (4) The supply of weapons market in Southeast Asia bears the imprint of the Cold War period, and the competition is becoming increasingly fierce; (5) The Southeast Asian arms trade network mainly includes four associations with Singapore and the United States, Vietnam and Russia, Indonesia and South Korea, Myanmar and China as the main arms importing and exporting countries respectively. Although other members of the association were unstable in different periods, they still accord with the basic characteristics of the East and West arms trade camp on the whole. [ABSTRACT FROM AUTHOR]

Traditional centrality metrics consider only shortest paths, neglecting alternative paths that can be strategic to maintain network connectivity. This paper proposes the disjoint multipath closeness centrality, a new metric to compute node centrality that extrapolates the traditional closeness to consider multiple shortest and disjoint quasi-shortest paths. The idea is to identify nodes that are close to all other nodes and are multiply-connected, which is important to perform high availability tasks. We limit the number of multiple disjoint paths using a connectivity factor φ . We comparatively investigate the correlation between our metric, the traditional closeness, and the information centrality using social and communication networks. We also assess the node ranking obtained by each metric and evaluate node reachability when one or multiple network failures occur. The results show that our metric maintains high concordance with the other closeness metrics but it can reclassify at least 59% of nodes in the evaluated networks. Our metric indeed identifies better-connected nodes, which remain more accessible when failures happen. [ABSTRACT FROM AUTHOR]

This paper presents research that focuses on nonflow contribution subtraction in heavy-ion collisions, using a multiphase transport model (AMPT). Specifically, the study aims to investigate the behavior of charged particle elliptic flow ( v 2 ) in d-Au collisions at a collision energy of s NN = 200 GeV and to determine the impact of nonflow sources, such as jet correlations and resonance decays, in small collision systems. To reduce nonflow effects, the per-trigger yield distribution in peripheral d-Au collisions or pp collisions with the same collision energy is subtracted. Our results show that the nonflow effects in central and mid-central collisions are not strongly dependent on subtracting the per-trigger yield distribution in peripheral d-Au collisions or pp collisions. Furthermore, the elliptic flow of charged particles, after removing nonflow effects through two subtracting methods from this work, exhibits consistency in various collision centrality classes. We also discuss comparisons with measurements from d-Au collisions at s NN = 200 GeV. Overall, this work provides valuable insights and serves as a reference for researchers studying nonflow contribution subtraction in experiments with small collision systems. [ABSTRACT FROM AUTHOR]

The use of statistical tools for predicting the winner in tennis matches has enjoyed an increase in popularity over the last two decades and, currently, a variety of methods are available. In particular, paired comparison approaches make use of latent ability estimates or rating calculations to determine the probability that a player will win a match. In this paper, we extend this latter class of models by using network indicators for the predictions. We propose a measure based on eigenvector centrality. Unlike what happens for the standard paired comparisons class (where the rates or latent abilities only change at time t for those players involved in the matches at time t), the use of a centrality measure allows the ratings of the whole set of players to vary every time there is a new match. The resulting ratings are then used as a covariate in a simple logit model. Evaluating the proposed approach with respect to some popular competing specifications, we find that the centrality-based approach largely and consistently outperforms all the alternative models considered in terms of the prediction accuracy. Finally, the proposed method also achieves positive betting results. [ABSTRACT FROM AUTHOR]

A co-location pattern is a set of spatial features whose instances are frequently correlated to each other in space. Its mining models always consist of two essential steps. One step is to generate neighbor relationships between spatial instances, and another step is to check the prevalence of candidate patterns on the clique, star or Delaunay triangulation relationships. At least three major issues are addressed in this paper. First, since different spatial regions, different distribution densities, it is difficult to set appropriate parameters to generate ideal neighbor relationships. Second, the clique relationship and the others are so strongly rigid that the users' personal interests are suppressed; some interesting patterns are neglected without increasing redundancy. Third, the different strength of correlations among instances are neglected in prevalence calculation. It causes correlations among features to be undifferentiated. Accordingly, the main work of this paper includes: (1) The neighbor relationship generation can be improved on the idea that the distances between an instance and any of its neighbors are not remarkably different. (2) The type- β co-location pattern is defined and checked based on a co-occurrence where the closeness centrality of each instance is not less than a given threshold β. (3) Since the closeness centrality carries strength of correlations among instances, the strength of the correlations between a feature and the other ones in a type- β co-location pattern can be evaluated with prevalence calculation. Finally, experiments on synthetic and real-world spatial data sets are used to assess the effectiveness and efficiency of our works. The results show that fewer spatial neighbor relationships are generated, and more interesting patterns can be discovered by flexibly adjusting β according to the user's preferences. [ABSTRACT FROM AUTHOR]

This research paper aims to provide a summary of the luost important challenges facing the entity of the Islamic nation in the modem era, and given tire importance and centrality of reconciliatioir hr tire Islairric public order, many scholars and reformers have worked to spread the spirit of harmony Iir order to airalyze tlris paper aird discuss its contents, I adopted tire descriptive-analytical approach, where tire irrost important pivotal issues that stir the dispute were referred to, then discussed ways to avoid obstacles to reconciliatioir witlr a legitimate approaclr. It is iroted that tire dispute between Muslims is due to tire failure to deal iir major aird cmcial issues for tire iratioir iir a way tlrat brings iirterests to its individuals aird groups, aird wards them froirr tire evils arising from tire inability to manage intellectual and doctrinal differences according to consensual approaches of interest. This paper is the foundations and premises aimed at gatlrering the word and overcoming the major and minor differences between Muslims when required by the immediate legal necessity, with reference to the most prominent fruits that the nation reaps as a result of rallying around its common and constituent. The research concluded that it is necessary to reconsider the premises and reasons that call for disagreement and dissent, and that the different parties to the legal texts should apply scientific controls that are abstract fiom ideological quarrels, and to stay away as much as possible from the causes of the emergence of disagreement, and work to bring the views closer by the irrodem available irreans. Such as holding conferences and seminars, and broadcasting the discourse of harmony in modern technical means. [ABSTRACT FROM AUTHOR]

The aim of the paper is to discuss an issue of urban centralities not only in spatial but also in temporal terms. We seek to overcome the traditional view of urban centralities as materialised places. We argue that prominence of place is closely tied to a certain time regime or rhythm and that some prominent times tend to be spatialised through certain urban places. We emphasise the hybrid spatiotemporal nature of urban centrality that emerges from the inseparable coupling of the spatial and temporal dimensions of urban everydayness. The paper seeks to introduce timing, synchronisation and rhythms as important constituents of the urban tissue. The methodology links together Lefebvre‐inspired rhythmanalysis and the concept of chronotope. A complex dataset that depicts the aggregated rhythms of people's presence in selected locales is employed to demonstrate multiplicity of prominent times present in the contemporary city. The chronotopes are narrated as recurrent situations in which specific urban locales and specific times are animated and linked together through the presence of interacting individuals. The elusive spatiotemporal centrality is reflected in the story of the chronotope, describing its rhythm, scale, pacemakers, and actors. [ABSTRACT FROM AUTHOR]

Purpose: With the aim to enrich the ongoing debate about healthcare management, the paper has a twofold intent: [1] to emphasise the interpretative contribution that intellectual capital can provide to a better understanding of the relevant role of patients in the healthcare sector and [2] to investigate the relationships between the three main dimensions of intellectual capital – human capital, relational capital and structural capital – and patient satisfaction in the healthcare sector. Design/methodology/approach: The intellectual capital framework is contextualised in the healthcare sector, and the relationships among patient evaluations of human capital, relational capital and structural capital and patient satisfaction are tested via structural equation modelling (SEM) using primary data collected with reference to a sample of 561 Italian patients involved in post survey treatments in three Italian hospitals. Findings: The role of intellectual capital in supporting a better understanding of processes and dynamics of patient satisfaction in the healthcare sector is underlined. The empirical research provides possible guidelines for recovery patients centrality in healthcare management. Originality/value: The paper shows how an intellectual capital framework can support a better understanding and management of dynamics and processes through which patient centrality and satisfaction in healthcare management can be enforced. [ABSTRACT FROM AUTHOR]

Keyword extraction is one of the most important aspects of text mining. Keywords help in identifying the document context. Many researchers have contributed their work to keyword extraction. They proposed approaches based on the frequency of occurrence, the position of words or the similarity between two terms. However, these approaches have shown shortcomings. In this paper, we propose a method that tries to overcome some of these shortcomings and present a new algorithm whose efficiency has been evaluated against widely used benchmarks. It is found from the analysis of standard datasets that the position of word in the document plays an important role in the identification of keywords. In this paper, a fuzzy logic-based automatic keyword extraction (FLAKE) method is proposed. FLAKE assigns weights to the keywords by considering the relative position of each word in the entire document as well as in the sentence coupled with the total occurrences of that word in the document. Based on the above data, candidate keywords are selected. Using WordNet, a fuzzy graph is constructed whose nodes represent candidate keywords. At this point, the most important nodes (based on fuzzy graph centrality measures) are identified. Those important nodes are selected as final keywords. The experiments conducted on various datasets show that proposed approach outperforms other keyword extraction methodologies by enhancing precision and recall. [ABSTRACT FROM AUTHOR]

The literature on social learning examines how people learn from their neighbors and reach a consensus. The DeGroot social learning model describes the social learning process as one in which people form their opinions by taking a weighted average of their neighbors' opinions. In the model, the influence structure is represented by a single matrix. In this paper, we empirically identify the role of the hub and authority centralities based on this matrix using data on microfinance adoption in rural Indian villages. Controlling for other well-known centrality measures, authority centrality is positively associated with final adoption rates in the villages, but hub centrality is not. Furthermore, we find that authority centrality is the most informative variable predicting microfinance diffusion success from LASSO regressions. [ABSTRACT FROM AUTHOR]

The human body is central to the existence of dress in all its forms in cultures and societies and situated in space and time. While the main purpose of fashion is the continuous iteration, revision, and reinvention of the dress, the centrality of the body in fashion and dress practices is problematic, particularly because bodies and garments relate not only as material entities but also through intangible and immanent agencies. Although this centrality has been questioned by some scholars, the articulation of new paradigms of the dressed body within the nature-culture continuum remains underexplored. Here I am referring to a reconsideration of the body in the larger ecosystem of natural entities and phenomena within which it exists. Such a reconsideration offers a broader field of possibilities to articulate bodies, dress and ways in which they relate and come together. Informed by natural sciences, this paper investigates the notion of symbiosis, which is a way of living together of different organisms, to construct a new paradigm for understanding how the body and the dress interact and form each other. [ABSTRACT FROM AUTHOR]

Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively. [ABSTRACT FROM AUTHOR]

Building on Alfred Marshall's claim that political economy should be a science of activities rather than a science of wants, the paper proposes to ground political economy on the centrality of work. Work is central in three different senses: (i) factual — work plays a central role in human lives and societies; (ii) epistemic — giving work center stage has constitutive theoretical effects; (iii) political — work is a primary tool of and for the transformation of society. Grounding political economy on work entails prioritizing economic agents as workers/producers over economic agents as consumers and acknowledging that changes in the world of work are major drivers of history. This contrasts with mainstream economics, which has always overlooked the activity of work, giving priority to consumer sovereignty and treating work as if it were a commodity. We propose a concept of work, based on Dejours' approach, that highlights the key role of work organization in making work a source of fulfillment versus suffering. The arguments and concepts proposed lead to advocating the participation of workers in firm governance. [ABSTRACT FROM AUTHOR]

Due to the contractual obligations of Integrated Project Delivery (IPD), participants are intrinsically motivated to collaborate with each other. This paper presents the findings of an ethnographic case study exploring the communication structures among members of an IPD team. Direct observational data were collected from three different types of team meetings over a period of 3 months. The data were coded and analyzed using social network analyses (SNA). Findings highlighted the characteristics of each type of communication network and the relation between them. While participants affiliated with the owner, designers, general contractor, and trade partners were members of the integrated team, members representing the general contractor emerged to take central positions in both formal and informal networks acting as the primary channel of information exchange and social relationships. Further, quadratic assignment procedure multiple regression (QAP-MR) revealed that informal communications among the team members could significantly predict the strength of formal communications. The graphical visualizations and the empirical evidence provide insights for formal and informal communication structures with their distinctive features and their interrelationships. [ABSTRACT FROM AUTHOR]

The idea of centrality measurements is quite appropriate for determining the important vertices or edges in a network. A vertex in a network may be an important vertex depending on its angle of assumption. There are many centrality measurements to find the characteristics of a vertex in a network. Betweenness centrality is an important variant of centrality measurement for analyzing complex networks based on shortest paths. The betweenness centrality of a node point u is the sum of the fraction which has the number of shortest paths between any two node points v and w as denominator and the number of the shortest paths passing through the vertex u between them as numerator. This paper describes some theoretical results relating to the betweenness centrality and relative betweenness centrality of different types of corona graphs (Pn⊙Pm, Pn⊙Km, Cn⊙Km, Cn⊙Pm, Cn⊙Cm and Cn⊙Kl,m) and unicyclic graphs (A(n, k, l), B(n, k, l), D(n, k, l) and E(n, k, l)). [ABSTRACT FROM AUTHOR]

This paper proposes a new synthetic measure of node centrality, namely, multi-information fused degree of grey incidence centrality (MIFDC), which is used to evaluate the importance of nodes and identify influential nodes in complex networks, It is the jirst time that the grey incidence analysis (GIA) and the D-S evidence theory are combined to identify influential nodes in complex networks in the MIFDC method. The proposed MIFDC measure comprehensively considers the information of multiple centrality measures and can correct the subjective bias problem in the selection process of the grey incidence operator. To verify the performance of the proposed method, the MIFDC method is applied to identify influential nodes in two real networks, the Advanced Research Project Agency (ARPA) network, and the terrorist relationship network. The application results show that the MIFDC method can effectively identify the influential nodes of the network. [ABSTRACT FROM AUTHOR]

The heterogeneous nature indicates that different nodes may play different roles in network structure and function. Identifying influential spreaders is crucial for understanding and controlling the spread processes of epidemic, information, innovations, and so on. So how to identify influential spreaders is an urgent and crucial issue of network science. In this paper, we propose a novel local-information-based method, which can obtain the degree information of nodes' higher-order neighbors by only considering the directly connected neighbors. Specifically, only a few iterations are needed to be executed, the degree information of nodes' higher-order neighbors can be obtained. In particular, our method has very low computational complexity, which is very close to the degree centrality, and our method is of great extensibility, with which more factors can be taken into account through proper modification. In comparison with the well-known state-of-the-art methods, experimental analyses of the Susceptible-Infected-Recovered (SIR) propagation dynamics on ten real-world networks evidence that our method generally performs very competitively. [ABSTRACT FROM AUTHOR]

As the final piece of scholarship in the special issue, this paper pulls together data from the Aboriginal Voices project to analyse how Aboriginal students in Australia today experience schooling, particularly in relation to the futurity of their identity as sovereign First Nations Peoples. Using Decolonising Race Theory as a key methodological framework in this special issue enabled an assessment of the purpose and effects of coloniality to acknowledge the survival and innovation of First Nations Peoples in resisting and imagining a future otherwise. In doing so, the empirical data, and provocations, presented throughout this collection, opens up possibilities for exploring how the centrality of sovereignty impacts young Aboriginal students' interactions with and experienced success within the Australian schooling system. [ABSTRACT FROM AUTHOR]

Criminal networks continue to utilize the global financial system to launder their proceeds of crime, despite the broad enactment of anti-money laundering (aml) laws and regulations in many countries. Money laundering consumes capital resources and the tax revenue needed to fund infrastructure development and alleviate poverty in developing market economies. This paper, therefore, expands on the tools available for enabling privacy-preserving data mining in multidimensional datasets to combat cross-border money laundering. Most importantly, this paper develops a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes. The research used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and the symmetric-key encryption algorithm. The results support the argument that the proposed network measure can detect node anomalies in the cross-border financial flows network, enabling regulatory authorities and law enforcement agencies to investigate financial transactions for suspicious activity and criminal conduct. The encryption algorithm can ensure adherence to information privacy laws and policies without compromising data reusability. Hence, the proposed methodology can improve the proactive management of money laundering risks associated with cross-border fund flows for the global financial system’s benefit. [ABSTRACT FROM AUTHOR]

In this paper, the Sun and its behavior are studied by means of complex networks. The complex network was built using the Visibility Graph algorithm. This method maps time series into graphs in which every element of the time series is considered as a node and a visibility criterion is defined in order to connect them. Using this method, we construct complex networks for magnetic field and sunspots time series encompassing four solar cycles, and various measures such as degree, clustering coefficient, mean path length, betweenness centrality, eigenvector centrality and decay exponents were calculated. In order to study the system in several time scales, we perform both a global, where the network contains information on the four solar cycles, and a local analysis, involving moving windows. Some metrics correlate with solar activity, while others do not. Interestingly, those metric which seem to respond to varying levels of solar activity in the global analysis, also do in the moving windows analysis. Our results suggest that complex networks can provide a useful way to follow solar activity, and reveal new features on solar cycles. [ABSTRACT FROM AUTHOR]

The proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets. [ABSTRACT FROM AUTHOR]