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Several real-world phenomena, including social, communication, transportation, and biological networks, can be efficiently expressed as graphs. This enables the deployment of graph algorithms to infer information from such complex network interactions to enhance graph applications’ accuracy, including link prediction, node classification, and clustering. However, the large size and complexity of the network data limit the efficiency of the learning algorithms in making decisions from such graph datasets. To overcome these limitations, graph embedding techniques are usually adopted. However, many studies not only assume static networks but also pay less attention to preserving the network topological and centrality information, which information is key in analyzing networks. In order to fill these gaps, we propose a novel end-to-end unified Topological Similarity and Centrality driven Hybrid Deep Learning model for Temporal Link Prediction (TSC-TLP). First, we extract topological similarity and centrality-based features from the raw networks. Next, we systematically aggregate these topological and centrality features to act as inputs for the encoder. In addition, we leverage the long short-term memory (LSTM) layer to learn the underlying temporal information in the graph snapshots. Lastly, we impose topological similarity and centrality constraints on the model learning to enforce preserving of topological structure and node centrality role of the input graphs in the learned embeddings. The proposed TSC-TLP is tested on 3 real-world temporal social networks. On average, it exhibits a 4% improvement in link prediction accuracy and a 37% reduction in MSE on centrality prediction over the best benchmark.

The elliptic flow (v2) of produced particles is one of the important observables sensitive to the transport properties of the strongly interacting matter created in relativistic heavy-ion collisions. Detailed differential measurements of v2 are also foreseen in the future Multi-Purpose Detector (MPD) experiment at the Nuclotron based Ion Collider fAcility (NICA) at collision energies sNN = 4–11 GeV. Elliptic flow strongly depends on collision geometry, defined by the impact parameter b. Usually b is an input to theoretical calculations and can be deduced from experimental observables in the final state using the centrality procedure. In this work, we investigate the influence of the choice of centrality procedure on the elliptic flow measurements at NICA energies.

Objective/context: This study examines the influence of the centrality measures of deputies on the probability of success of legislative initiatives in the LXIII Legislature of the Chamber of Deputies in Mexico (2015-2018). Methodology: By analyzing the co-sponsorships and subscriptions of legislative initiatives, we constructed a network and estimated different centrality measures. Subsequently, we used logistic regression models to evaluate the effect of these variables on the probability of initiative approval. Conclusions: Our findings suggest that legislators with a high number of connections, as measured by degree-outputs, are more likely to see their initiatives approved, which is due to reciprocal relationships that arise from supporting other legislators’ bills. Originality: Our results—obtained from 5,275 observations—offer new insights into the influence of legislators’ connections and the success of their initiatives while broadening the existing understanding of the Mexican Legislative Branch.

Attempts to find central "influencers," "opinion leaders," "hubs," "optimal seeds," or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion.

ObjectiveWe aimed to identify differences in network properties of white matter microstructure between asymptomatic ulcerative colitis (UC) participants who had a history of chronic gut inflammation, healthy controls (HCs) and a disease control group without gut inflammation (irritable bowel syndrome; IBS).DesignDiffusion weighted imaging was conducted in age and sex-matched participants with UC, IBS, and HCs (N = 74 each), together with measures of gastrointestinal and psychological symptom severity. Using streamline connectivity matrices and graph theory, we aimed to quantify group differences in brain network connectivity. Regions showing group connectivity differences were correlated with measures showing group behavioral and clinical differences.ResultsUC participants exhibited greater centrality in regions of the somatosensory network and default mode network, but lower centrality in the posterior insula and globus pallidus compared to HCs (q

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.

The increasing use of technology and rise of virtual work has fundamentally changed how employees interact with each other. No longer can employees reliably predict when and where their coworkers will work, transforming the very ways in which coworker relationships unfold over time. This is perhaps especially true for coworker multiplex relationships, which fuse a coworker relationship with a friendship relationship and strongly affect job satisfaction and turnover intentions. Through a qualitative study of a Fortune 500 technology firm with a largely remote workforce, we build theory on how virtual coworkers form friendships with each other on the path to multiplexity. Our emergent theory reveals the centrality of "relational cadence"—perceived convergence in the patterns of interaction between oneself and a particular coworker—to the establishment and growth of these relationships. It also differentiates work-related from friendship-related cadence, stresses the symbiosis of these cadences in multiplexity, and emphasizes the importance of temporal rhythm and understanding relational particulars (the nature of the specific coworker relationship) in the development of each form of cadence. These findings highlight how virtualization affects the experience of relating at work, and thereby make important contributions to literatures on relationships at work, coworker friendships, and virtual relationships. [ABSTRACT FROM AUTHOR]

This article provides new evidence on the relationship between design and innovation performance at the firm level. In particular, we integrate previous analyses of the link between design investments and innovation by considering the extent to which firms put design at the center of their business activities. Moreover, we distinguish between the effect of design on innovation and its effect on the success of innovation, as captured by firms' innovative turnover. The use of the European Innobarometer survey, covering a unique set of questions on the topic, allows us to test a set of hypotheses about these relationships on a large sample of firms. The results show that a firm's approach to design plays an important role in its propensity to innovate: the more central the role of design within a firm, the higher the likelihood it innovates. The same holds true when considering the share of turnover from innovation. However, sales associated with innovation do not increase linearly with design investments, as we find a positive effect only for firms investing intensively in design. Overall it emerges that the centrality of design is strongly associated with firms' innovation performance, while design investment per s e has a more nuanced role. [ABSTRACT FROM AUTHOR]

Background: Regular physical activity (PA) has many benefits for children and adolescents, yet many do not meet PA recommendations. Social context is important for promoting or discouraging PA among children and adolescents. This review aimed to identify social network variables related to PA among children and adolescents. Methods: A systematic review of the literature was conducted in September 2018 using PsycINFO, MEDLINE, PubMed, and Web of Science. Included articles needed to (1) be focused on children (aged 5–11 y) or adolescents (aged 12–17 y), (2) include a measure of PA, (3) include a measure of egocentric or sociocentric social connection in which alters were nominated, and (4) perform an analysis between network data and PA. Results: A search of 11,824 articles was refined to a final sample of 29 articles. Social network themes and concepts such as homophily, centrality, and network composition were related to child and adolescent PA behavior across the literature. Conclusions: The impact of an individual's social network is evident on their PA behaviors. More research is needed to examine why these networks form in relation to PA and how interventions can utilize social network analysis to more effectively promote PA, especially in underserved and minority populations. [ABSTRACT FROM AUTHOR]

The primary purpose of this study was to examine the perceptions of commuter student athletes who were thought to experience a multiplicity of institutional logics while competing at the NCAA Division II level. Second, if multiple competing logics were present, the authors intended to test a theoretical model of logic multiplicity development. Utilizing a case study methodology, the authors collected data from commuter student athletes competing at the Division II level. The study's findings indicated that commuter student athletes perceived the presence of multiple competing logics and that these logics indicated an aligned or minimally estranged organization. In particular, the high compatibility and high centrality of multiple competing logics signified an organization aligned between academic, athletic, and family values, whereas the low centrality and low compatibility of social identification and societal factors denoted an estranged organization for commuter student athletes. The implications of this research within sport management are presented herein. [ABSTRACT FROM AUTHOR]

IntroductionSome events are remembered as more central to a person’s identity than others. However, it is not entirely clear what characterizes these autobiographical memories central to one’s identity. In this study, we examined the effects of various characteristics on centrality to identity of positive and negative memories. Characteristics such as emotionality, vividness, and how frequently a memory is retrieved and shared with others as well as ruminative and reflective self-foci were studied.MethodsThe sample included 356 participants (18–92 years of age). First, participants responded to demographic questions and individual difference questionnaires. Next, they recalled memories in response to 12 emotional cue words. The cue words were balanced for emotional valence (i.e., six positive and six negative) and presented in a random order. After retrieving all memories, participants rated them regarding centrality, using the short seven-item Centrality of Event Scale and other memory characteristics, on a seven-point Likert scale. Multivariate multilevel regression was used for data analyzes, to consider multiple characteristics at the same time and account for data dependency within individual.ResultsThe results showed that emotionality, vividness, and frequency of memory retrieval contributed to higher centrality of memories, and employing a reflective self-focus resulted in rating memories as more central. In specific cases, these characteristics were associated differently with centrality of positive and negative memories.DiscussionCentral memories can be perceived as markers in a person’s life story. The findings of this study suggest that these marker events are also highly available in a person’s memory system, by being actively emotional, visually rich, and frequently retrieved. Moreover, not only memory characteristics but also individual’s features are important to fully understand the autobiographical memory centrality.

Informality plays an imperative role in offering housing for households in developing countries when the formal market cannot provide enough to keep up with residents' demands. The actors' interaction, with one another, plays an imperative role in land transactions in informal settlement areas. As informal actors operate outside the formal land transactions their activities and methods of operation are rarely understood. Therefore, based on social network theory, this paper aims to identify and examine informal actors, their functions, interactions, and power relationships in informal settlement areas. To this end, this study employed key informant interviews, focus group discussions, structured questionnaires, and a review of published literature, as well as official documents. In the study area, most residents acquired land through informal mechanisms. Major actors include farmers, local land administrators, speculators, land brokers, residents, government officials, and religious leaders. The study also uncovers that the role of each actor varies from information provision to price fixing. Their roles and interactions are governed by dynamic networks and occasionally overlap functions. Among the network actors, land brokers are considered the most influential and powerful because they possess a high degree of centrality, closeness, betweenness, and eigenvector. They hold a pivotal position in the network and act as a liaison between the network's actors. Therefore, the roles of land brokers, who often actively influence the land transaction process, should be considered in urban land governance and incorporated in policy formulation and implementation.

In this study, we investigate the effect of weight thresholding (WT) on the robustness of real-world complex networks. Here, we assess the robustness of networks after WT against various node attack strategies. We perform WT by removing a fixed fraction of weak links. The size of the largest connected component indicates the network's robustness. We find that real-world networks subjected to WT hold a robust connectivity structure to node attack even for higher WT values. In addition, we analyze the change in the top 30% of central nodes with WT and find a positive correlation in the ranking of central nodes for weighted node centralities. Differently, binary node centralities show a lower correlation when networks are subjected to WT. This result indicates that weighted node centralities are more stable indicators of node importance in real-world networks subjected to link sparsification. [ABSTRACT FROM AUTHOR]

The current 6-month follow-up study investigated 1) the relationship between posttraumatic stress disorder (PTSD) at baseline (T1), trauma centrality and two types of cognitive emotion regulation (CER) at 3-month follow-up (T2), and psychiatric co-morbidity and posttraumatic growth (PTG) at 6-month follow-up (T3), 2) whether trauma centrality at T2 would mediate the impact of initial PTSD on psychiatric co-morbidity and PTG at T3, and 3) whether the two types of CER at T2 (i.e., adaptive CER and maladaptive CER) would respectively mediate the effect of initial PTSD on psychiatric co-morbidity and PTG at T3. Seven hundred and fifty-seven traumatized Chinese adolescents (Male = 400, Female = 357) from two secondary schools participated in the study and completed a demographic page, the Posttraumatic Stress Disorder Checklist for DSM-5, the Centrality of Events Scale, the Posttraumatic Growth Inventory, the General Health Questionnaire-28, the Cognitive Emotion Regulation Questionnaire, and the Educational Stress Scale for Adolescents. After controlling for demographic variables and academic stress, PTSD at baseline was positively associated with trauma centrality at T2, two types of CER at T2, and PTG at T3, but negatively related to psychiatric co-morbidity at T3. Trauma centrality at T2 did not mediate the impact of initial PTSD on psychiatric co-morbidity and PTG at T3. Both types of cognitive emotion regulation at T2 (i.e., adaptive CER and maladaptive CER) respectively mediated the effect of initial PTSD on PTG at T3 and but not that on psychiatric co-morbidity at T3. Following past trauma, Chinese adolescents might experience psychological distress as well as positive changes over time. These traumatic outcomes could be affected by adolescents' thinking patterns about trauma, as opposed to by their concept of self. Adaptive thinking patterns promoted the positive effect of trauma onto personal growth, whereas the maladaptive patterns impaired the development of growth. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We show that prominent centrality measures in network analysis are all based on additively separable and linear treatments of statistics that capture a node's position in the network. This enables us to provide a taxonomy of centrality measures that distills them to varying on two dimensions: (i) which information they make use of about nodes' positions, and (ii) how that information is weighted as a function of distance from the node in question. The three sorts of information about nodes' positions that are usually used—which we refer to as "nodal statistics"—are the paths from a given node to other nodes, the walks from a given node to other nodes, and the geodesics between other nodes that include a given node. Using such statistics on nodes' positions, we also characterize the types of trees such that centrality measures all agree, and we also discuss the properties that identify some path-based centrality measures. [ABSTRACT FROM AUTHOR]

Parties make hundreds of campaign promises but not all are seen by voters as central to a party's offering. Studies of government promise fulfillment accept that not all promises are equivalent but in practice treat all promises equally because they lack an appropriate means of measuring promise centrality. To demonstrate the importance of accounting for pledge fulfillment, we develop a conjoint experiment method to measure public opinion about promise centrality which can be used to construct centrality weights. We demonstrate this approach's utility by examining the 2017 UK Conservative manifesto. Centrality weighting reduces our assessment of Conservative promise keeping by 21 percentage points (1.3 standard deviations of typical promise-completion rates found in comparative studies). Weighting increases the centrality of EU promises sevenfold and immigration promises sixfold, and reduces the centrality of miscellaneous administrative promises by more than half. These results illustrate that pledge centrality cannot be ignored when assessing pledge fulfillment. [ABSTRACT FROM AUTHOR]

Terabytes of data are now being handled by an increasing number of apps, and rapid user decision-making is hampered by data analysis. At the same time, there is a rise in interest in big data analysis for social networks at the moment. Thus, adopting distributed multi-agent-based technology in an optimum way is one of the solutions to effective big data analysis for social networks. Studying the development of a social network helps users gain an understanding of interactions and relationships and guides them in making decisions. In this study, a method called Hessian Distributed Ant Optimized and Perron–Frobenius Eigen Centrality (HDAO-PFEC) is developed to analyze large amounts of data (i.e., Big Data) in a computationally accurate and efficient manner. Designing an adaptable Multi-Agent System architecture for large data analysis is the primary goal of HDAO-PFEC. Initially, using a Hessian Mutual Distributed Ant Optimization MapReduce model, comparable user interest tweets are produced in a computationally efficient manner. Eigen Vector Centrality is a measure of a node's importance in a network (i.e., a social network), which allows association with other significant nodes (i.e., users), allowing for a greater effect on social networks. With this goal in mind, a MapReduce methodology in the Hadoop platform using Big Data, which enables quick and ordered calculations, is used in a distributed computing method to estimate the Eigen Vector Centrality value for each social network member. Lastly, extensive investigative experimental learning demonstrates the HDAO-PFEC method's use and accuracy as well as its time and overhead on the well-known sentiment 140 dataset. [ABSTRACT FROM AUTHOR]

To explore the local spontaneous neural activity and whole-brain functional connectivity patterns in the resting brain of acrophobia patients. 50 patients with acrophobia and 47 healthy controls were selected for this study. All participants underwent resting-state MRI scans after enrollment. The imaging data were then analyzed using a voxel-based degree centrality (DC) method, and seed-based functional connectivity (FC) correlation analysis was used to explore the correlation between abnormal functional connectivity and clinical symptom scales in acrophobia. The severity of symptoms was evaluated using self-report and behavioral measures. Compared to controls, acrophobia patients showed higher DC in the right cuneus and left middle occipital gyrus and significantly lower DC in the right cerebellum and left orbitofrontal cortex (p < 0.01, GRF corrected). Additionally, there were negative correlations between the acrophobia questionnaire avoidance (AQ- Avoidance) scores and right cerebellum-left perirhinal cortex FC (r = −0.317, p = 0.025) and between scores of the 7-item generalized anxiety disorder scale and left middle occipital gyrus-right cuneus FC (r = −0.379, p = 0.007). In the acrophobia group, there was a positive correlation between behavioral avoidance scale and right cerebellum-right cuneus FC (r = 0.377, p = 0.007). The findings indicated that there are local abnormalities in spontaneous neural activity and functional connectivity in the visual cortex, cerebellum, and orbitofrontal cortex in patients with acrophobia. [ABSTRACT FROM AUTHOR]

The significance of the PageRank algorithm in shaping the modern Internet cannot be overstated, and its complex network theory foundations continue to be a subject of research. In this article, we carry out a systematic study of the structural and parametric controllability of PageRank's outcomes, translating a spectral graph theory problem into a geometric one, where a natural characterization of its rankings emerges. Furthermore, we show that the change of perspective employed can be applied to the biplex PageRank proposal, performing numerical computations on both real and synthetic network datasets to compare centrality measures used. [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]

Purpose: To explore the alterations of whole brain functional network using the degree centrality (DC) analysis in neovascular glaucoma (NVG) and the correlation between DC values and NVG clinical indices. Materials and methods: Twenty NVG patients and twenty normal controls (NC), closely matched in age, sex, and education, were recruited for this study. All subjects underwent comprehensive ophthalmologic examinations and a resting-state functional magnetic resonance imaging (rs-fMRI) scan. The differences in DC values of brain network between NVG and NC groups were analyzed, and correlation analysis was performed to explore the relationships between DC values and clinical ophthalmological indices in NVG group. Results: Compared with NC group, significantly decreased DC values were found in the left superior occipital gyrus and left postcentral gyrus, while significantly increased DC values in the right anterior cingulate gyrus and left medial frontal gyrus in NVG group. (All P < 0.05, FDR corrected). In the NVG group, the DC value in left superior occipital gyrus showed significantly positive correlations with retinal nerve fiber layer (RNFL) thickness (R = 0.484, P = 0.031) and mean deviation of visual field (MDVF) (R = 0.678, P = 0.001). Meanwhile, the DC value in the left medial frontal gyrus demonstrated significantly negative correlations with RNFL (R = − 0.544, P = 0.013) and MDVF (R = − 0.481, P = 0.032). Conclusions: NVG exhibited decreased network degree centrality in visual and sensorimotor brain regions and increased degree centrality in cognitive-emotional processing brain region. Additionally, the DC alterations might be complementary imaging biomarkers to assess disease severity. [ABSTRACT FROM AUTHOR]

Identifying the seed nodes in networks is an important task for understanding the dynamics of information diffusion. It has many applications, such as energy usage/consumption, rumor control, viral marketing, and opinion monitoring. When compared to other nodes, seed nodes have the potential to spread information in the majority of networks. To identify seed nodes, researchers gave centrality measures based on network structures. Centrality measures based on local structure are degree, semi-local, Pagerank centralities, etc. Centrality measures based on global structure are betweenness, closeness, eigenvector, etc. Very few centrality measures exist based on the network's local and global structure. We define mixed centrality measures based on the local and global structure of the network. We propose a measure based on degree, the shortest path between vertices, and any global centrality. We generalized the definition of our mixed centrality, where we can use any measure defined on a network's global structure. By using this mixed centrality, we identify the seed nodes of various real-world networks. We also show that this mixed centrality gives good results compared with existing basic centrality measures. We also tune the different real-world parameters to study the effect of their maximum influence. [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]

One of the most important problems in complex networks is the location of nodes that are essential or play a main role in the network. Nodes with main local roles are the centers of real communities. Communities are sets of nodes of complex networks and are densely connected internally. Choosing the right nodes as seeds of the communities is crucial in determining real communities. We propose a new centrality measure named density-based entropy centrality for the local identification of the most important nodes. It measures the entropy of the sum of the sizes of the maximal cliques to which each node and its neighbor nodes belong. The proposed centrality is a local measure for explaining the local influence of each node, which provides an efficient way to locally identify the most important nodes and for community detection because communities are local structures. It can be computed independently for individual vertices, for large networks, and for not well-specified networks. The use of the proposed density-based entropy centrality for community seed selection and community detection outperforms other centrality measures. [ABSTRACT FROM AUTHOR]

Developing a tailor-made centrality measure for a given task requires domain- and network-analysis expertise, as well as time and effort. Thus, automatically learning arbitrary centrality measures for providing ground-truth node scores is an important research direction. We propose a generic deep-learning architecture for centrality learning which relies on two insights: 1. Arbitrary centrality measures can be computed using Routing Betweenness Centrality (RBC); 2. As suggested by spectral graph theory, the sound emitted by nodes within the resonating chamber formed by a graph represents both the structure of the graph and the location of the nodes. Based on these insights and our new differentiable implementation of Routing Betweenness Centrality (RBC), we learn routing policies that approximate arbitrary centrality measures on various network topologies. Results show that the proposed architecture can learn multiple types of centrality indices more accurately than the state of the art. [ABSTRACT FROM AUTHOR]

Mudeungsan National Park is a tall mountain located in the middle of Gwangju City. It is a UNESCO Global Geopark recognised as a rich repository of biological resources with high plant species diversity. However, hiking trails created around the exposed bedrock on the slopes around the mountain's summit cause soil loss and habitat destruction. To conserve the plant ecosystem of Mudeungsan National Park sustainably, the present study was conducted to find a species that could indicate the conservation status of the ecosystem. We the sociological relationships between plants with the aim of restoring habitats and species diversity around the summit bedrock at Mudeungsan National Park. In particular, our goal was to provide basic data for projects aimed at improving future hiking trails in national parks by analysing the sociological relationships between Potentilla ancistrifolia var. dickinsii, which is a phytogeographically important plant, and its companion plants. Findings showed high centrality of Miscanthus Sinensis, Lespedeza bicolor, Rhododendron schlippenbachii, Carex lanceolate, Arundinella hirta, and Rubus crataegifolius in the plant social network on the summit ridge of Mudeungsan. The analysis of the plant social network around P. ancistrifolia var. dickinsii showed that the extent of destruction of the ridge ecosystem could be determined by the changes in P. ancistriflolia. var. dickinsii cover, indicating that this species could be used as an indicator species. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or 'teleport' directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network. [ABSTRACT FROM AUTHOR]

Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. Several studies show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi‐core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this article, we show novel parallel algorithms for updating the betweenness‐ and closeness‐centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness‐ and closeness‐centrality. For the latter, we also introduce techniques based on traversals of the block‐cut tree of a graph. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24‐core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. In addition, we conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real‐world graphs indicate that our algorithms achieve a significant speedup over corresponding state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Background: Limited studies have explored the relationship among cross-organizational and multidisciplinary medical staff. Aim: The present study conducted an in-depth examination and validation of the influence of complex cross-organization and multidisciplinary social networks on the job performance of team members. Method: Multi-level hierarchical regression analysis was used to assess the impact of the centrality and the characteristics of structural holes in social networks (i.e., advice network, information network, friendship network, and trust network) on job performance. Results: The in-closeness centrality of the advice network (β = 0.176, p < 0.05) and the betweenness centrality of the trust network (β = 0.126, p < 0.05) had positive effects on task performance. The in-closeness centrality of the advice network (β = 0.226, p < 0.05; β = 0.213, p < 0.05) and the CI (1 − constraint index) of the friendship network (β = 0.130, p < 0.05; β = 0.132, p < 0.05) had positive effects on contextual performance and overall job performance. Meanwhile, the out-closeness centrality of the information network (β = −0.368, p < 0.01; β = −0.334, p < 0.05) had a negative effect on contextual performance and overall job performance. Conclusions: This study investigates the relationship between healthcare professionals' job performance and their social networks, taking into account the perspectives of cross-organizational and multidisciplinary teams. The study contributes to the effort of breaking down barriers between different disciplines and organizations, and ultimately, improving the quality of healthcare delivery. [ABSTRACT FROM AUTHOR]

The link between entrepreneur's network centrality and innovation performance has been broadly studied in knowledge-intensive industries such as biotechnology. However, little research has been focused on the social mechanisms that allow innovators to reach such a central position. We contribute to the existing literature by exploring the factors that may lead or prevent entrepreneurs from reaching a central position in their professional networks of knowledge exchange and social support in French biotech milieu. We use a unique quantitative and qualitative database of 138 and 126 biotech entrepreneurs observed, respectively, in 2008 and 2013. When accounting for entrepreneurs' position in the social (friendship) and knowledge (advice) domain, we draw on three dimensions through which entrepreneurs build their position: their professional experience, their inter-organizational (or political) engagement, and the financial and geographical situation of their company. Results from a regression analysis showed that the specific individual and organizational aspects of the trajectory of the entrepreneurs explain their position in the observed networks. Factors such as the previous experience in the health industry, the training expertise, the international experience, the political engagement, and the geographical and financial situation of the company help entrepreneurs to build up their centrality. The two observations allow us to describe indirectly the evolution of norms that are considered legitimated to carry out innovation in the biotech field. [ABSTRACT FROM AUTHOR]

Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex 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]

We examined whether Dark Triad (DT) traits moderate people's tendency to associate moral traits with their true self. We hypothesized that people high in DT traits would show a weaker tendency to view moral (vs. immoral) characteristics as central to their identity. Undergraduate participants (N = 345) rated the perceived identity centrality of positive/negative traits in domains of morality/competence, and completed measures of psychopathy, narcissism, and Machiavellianism. Positive moral (vs. immoral and positive competence) traits were seen as more identity central overall, but this effect was weaker among participants high in DT traits. Further, all DT traits negatively (positively) predicted the identity centrality of moral (immoral) traits. These findings extend work on true self-perceptions and moral identity in the Dark Triad. [ABSTRACT FROM AUTHOR]

The measurement of two-particle Bose–Einstein momentum correlation functions are presented using s NN = 5.02 TeV PbPb collision data, recorded by the CMS experiment in 2018. The measured correlation functions are discussed in terms of Lévy-type source distributions. The Lévy source parameters are extracted as functions of transverse mass and collision centrality. These source parameters include the correlation strength λ , the Lévy stability index α , and the Lévy scale parameter R. The source shape, characterized by α , is found to be neither Gaussian nor Cauchy. A hydrodynamic-like scaling of R is also observed. [ABSTRACT FROM AUTHOR]

This article investigates group fragmentation in the al-Qaeda and Islamic State ecosystems, employing network analysis to examine the impact of specific network conditions on the probability of a faction splitting. Using new datasets of faction–faction (FF) and terrorist–terrorist (TT) relationships, the article tests 18 hypotheses exploring connections between factional splits and the number, polarity, and strength of FF and TT relationships, among other factors. The article offers three major findings. First, a greater number of relationships between factions is positively correlated with the probability of fragmentation. Second, having a small or moderate number of a faction's members belonging to another faction increases the probability of a split, but more significant cross-factional membership decreases the probability. Third, both high-degree centrality of a faction's leader and significant variations in the degree centrality within a faction's leadership structure is correlated with increased probability of a split. [ABSTRACT FROM AUTHOR]

We study the complexity of local graph-centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, which we apply to PageRank and Heat Kernel, for constructing a low-variance score estimator through a local exploration of the graph. We obtain an algorithm that, given any node in any graph of n nodes and m arcs, with probability (1 - \delta ) computes a multiplicative (1\pm \epsilon )-approximation of its score by examining only O\~(min(n 1/2\Delta 1/2 , n 1/2m1/4 )) nodes/arcs, where \Delta is the maximum outdegree of the graph and poly(\epsilon - 1 ) and polylog(\delta - 1 ) factors are omitted for readability. A similar bound holds for computational cost. We also prove a lower bound of \Omega (min(n 1/2\Delta 1/2, n1/3m1/3 )) for both query complexity and computational complexity. Moreover, in the jump-and-crawl graphaccess model, our technique yields a O\~(min(n 1/2\Delta 1/2 , n2/3 ))-queries algorithm; we show that this algorithm is optimal up to a logarithmic factor-in fact, sublogarithmic in the case of PageRank. These are the first algorithms with sublinear worst-case bounds for general directed graphs and any choice of the target node. [ABSTRACT FROM AUTHOR]

[Display omitted] • FAME2, compared to EPM1, had confined beta-cortico-muscular coherence (CMC) and increased centrality index in the sensorimotor region contralateral to movement. • In FAME2, CMC distribution and increased centrality index could counteract the severity and the spreading of the myoclonus. • In FAME2, there was a main decline in the network connectivity indexes, possibly linked to neuropsychological comorbidities. Familial Adult Myoclonic Epilepsy (FAME) presents with action-activated myoclonus, often associated with epilepsy, sharing various features with Progressive Myoclonic Epilepsy (PMEs), but with slower course and limited motor disability. We aimed our study to identify measures suitable to explain the different severity of FAME2 compared to EPM1, the most common PME, and to detect the signature of the distinctive brain networks. We analyzed the EEG-EMG coherence (CMC) during segmental motor activity and indexes of connectivity in the two patient groups, and in healthy subjects (HS). We also investigated the regional and global properties of the network. In FAME2, differently from EPM1, we found a well-localized distribution of beta-CMC and increased betweenness-centrality (BC) on the sensorimotor region contralateral to the activated hand. In both patient groups, compared to HS, there was a decline in the network connectivity indexes in the beta and gamma band, which was more obvious in FAME2. In FAME2, better localized CMC and increased BC in comparison with EPM1 patients could counteract the severity and the spreading of the myoclonus. Decreased indexes of cortical integration were more severe in FAME2. Our measures correlated with different motor disabilities and identified distinctive brain network impairments. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown an increased interest in combining Statistical Process Control/Monitoring and Social Network Analysis. One approach to detect anomalies in social networks is to monitor some summary statistics of the network structure using control charts. We aim in this study to conduct a performance comparison among some network centrality measures (betweenness, closeness, and degree) in terms of their ability in detecting anomalies using control charts. Although directed networks include more information on the nodes communication than the undirected ones, they are rarely considered and evaluated in literature. Hence, the performance comparison is conducted assuming weighted and unweighted directed networks. Two network-level measures are used; the average and standard deviation for each metric. Our simulation results revealed that the degree centrality measure in most cases is recommended to be used for both weighted and unweighted networks. Also, it can generally be concluded that the average of a centrality measure performs better than the standard deviation in detecting anomalies. However, if the structural change is major in weighted networks specifically, we recommend the standard deviation of the centrality measure to be used. [ABSTRACT FROM AUTHOR]

This work proposes a sentence network-based approach for performing the task of multi-document text summarization. The sentences of the input set of documents are represented by the nodes of the network. Weighted edges are added between the nodes to represent the semantic similarity between the corresponding sentences. The network has a multilayer structure, where each layer corresponds to an individual input document. This helps in effective differentiation between the inter-document and intra-document edges. A hyperparameter, namely layering factor, has been used to alter the strength of inter-document connections through reinforcement or weakening. It is hypothesized that the summary sentence nodes must act as effective information spreaders in the sentence network. Summary generation is performed by identifying the influential nodes of the network using VoteRank scheme. A comparative study with different network measures, such as Weighted Degree, PageRank, Betweenness centrality, and Closeness centrality reveals the efficacy of the proposed VoteSumm technique for multi-document text summarization. Improved performance is observed when an additional pre-processing step of syntactic simplification is applied on the raw text. Performance is further improved when keyword information is included in the simplified texts. [ABSTRACT FROM AUTHOR]

The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ( N part ) in high-energy heavy-ion collisions. For this purpose, supervised learning using both deep neural network (DNN) and convolutional neural network (CNN) is performed with labeled data obtained by modeling relativistic heavy-ion collisions utilizing A Multi-phase Transport Model (AMPT). Event-by-event distributions of pseudorapidity and azimuthal angle of charged hadrons weighted by their transverse momentum are used as input to train the DL models. The DL models did remarkably well in predicting N part values with CNN slightly outperforming the DNN model. The Mean Squared Logarithmic Error (MSLE) for the CNN model (Model-4) is determined to be 0.0592 for minimum bias collisions and 0.0114 for 0–60% centrality class, indicating that the model performs better for semi-central and central collisions. Furthermore, the studied DL model is proven to be robust to changes in energy as well as model parameters of the input. The current study demonstrates that the data-driven technique has a distinct potential for determining centrality in terms of the number of participants in high-energy heavy-ion collision experiments. [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]

Massey's method is a rating system that provides individual teams or players ranking. It is known that Massey's rating is related to the Katz centrality measure of the networks. Analyzing the node centrality of networks provides useful information on how important a node is. This study investigates a sampling distribution of Massey's rating and provides the test statistics of the null hypothesis of equal rating against the alternatives. Monte Carlo simulations are performed to compare the power of the proposed test and show that the asymptotic distribution of the ratings is well approximated by the normal distributions. Real dataset applications using men's tennis ATP ratings are illustrated. [ABSTRACT FROM AUTHOR]

We present predictions of elliptic flow (v 2) of identified hadrons at mid-rapidity (| y | < 1.0) in Au+Au collisions at E lab = 35 A GeV using the Parton Hadron String Dynamics (PHSD) model. The transverse momentum ( p T ) dependence of identified hadron v 2 in minimum bias (0–80%) and three different centrality intervals (0–10%, 10–40%, and 40–80%) are presented. A clear centrality dependence of v 2 (p T) is observed for particles and anti-particles. We also present the p T dependence of v 2 difference (Δ v 2) between particles and corresponding anti-particles. A significant difference in v 2 values for baryons and anti-baryons is observed. Constituent quark scaling (NCQ) of v 2 is investigated in Au+Au collisions. We also present a v 2 (p T) ratio between the HSD and PHSD modes to explore the effect of hadronic and partonic interactions in the medium. These predictions are useful for interpreting the data measured in the Beam Energy Scan (BES) program at RHIC. They will also be useful for the future Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) and Multi-Purpose Detector (MPD) at the Nuclotron-based Ion Collider facility (NICA). [ABSTRACT FROM AUTHOR]

The first measurement of J / ψ and D 0 production in PbNe collisions by the LHCb experiment in its fixed-target configuration is reported. The production of J / ψ and D 0 mesons is studied with a beam of lead ions with an energy of 2.5 TeV per nucleon colliding on gaseous neon targets at rest, corresponding to a nucleon-nucleon centre-of-mass energy of s NN = 68.5 GeV . The J / ψ / D 0 production cross-section ratio is studied as a function of rapidity, transverse momentum and collision centrality. These data are compared with measurements from p Ne collisions at the same energy and show no difference in the observed J / ψ suppression trend when comparing p Ne and PbNe peripheral collisions with PbNe central collisions. [ABSTRACT FROM AUTHOR]

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi–Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős–Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts–Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics. [ABSTRACT FROM AUTHOR]

We provide a characterization of closeness centrality in the class of distance-based centralities. To this end, we introduce a natural property, called majority comparison , that states that out of two adjacent nodes the one closer to more nodes is more central. We prove that any distance-based centrality that satisfies this property gives the same ranking in every graph as closeness centrality. The axiom is inspired by the interpretation of the graph as an election in which nodes are both voters and candidates and their preferences are determined by the distances to the other nodes. • We propose an axiomatic characterization of closeness centrality. • It is based on a new natural axiom named majority comparison. • We interpret a graph as an election in which nodes are both voters and candidates. [ABSTRACT FROM AUTHOR]

With the rising popularity of virtual reality, big data, and artificial intelligence (AI), social avatars have become important elements in user-generated content on social platforms. Social avatars can make use of network centrality to influence users' perceptions and attitudes toward products, lifestyles, and even trends, influencing their behavioral decisions and imitation intentions. This study explored the effect of the strength of social avatar centrality on users' intentions to imitate based on social network and parasocial interaction theory. The study determined the following: first, avatar centrality is positively related to consumers' imitation intent toward avatars; second, avatar centrality is positively related to self-AI connections and parasocial relationships; third, self-AI connection is positively related to parasocial relationships; fourth, self-AI connection and parasocial relationships are positively related to consumers' imitation intent toward social avatars. The findings of this study can enrich the application of social network theory in AI and provide important insights for social network platforms and brand promotion to strengthen the social relationships between social avatars and users and the social influence of avatars. [ABSTRACT FROM AUTHOR]