Identification of influencers in complex networks by local information dimensionality

Wen, Tao; Deng, Yong

doi:10.1016/j.ins.2019.10.003

Cited by 107 publications

(48 citation statements)

References 46 publications

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“…Researchers have analysed network properties from different perspectives, depending on the type and complexity of networks. Entropy-based analysis has been used to identify influential nodes using local information dimensionality [37]. Fractal dimensions are being explored to determine the vulnerability of complex networks [38].…”

Section: Discussionmentioning

confidence: 99%

Social network analysis of COVID-19 transmission in Karnataka, India

et al. 2020

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We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11-40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1-47 for 199 (17.35%) nodes, and betweenness, 0.5-87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) 'super-spreaders' (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

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Section: Discussionmentioning

confidence: 99%

Social network analysis of COVID-19 transmission in Karnataka, India

et al. 2020

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“…Studies on influencer nodes reveal that such individuals help in fast information diffusion across their social networks because they are highly connected individuals with a higher degree centrality in their network [40], [41]. In a recent research, T. Wen and Y. Deng [42] investigated six real world complex networks to identify the influencer nodes using Shannon entropy measure. They found that individuals are more influential when they have a higher centrality in local information dimensionality.…”

Section: ) Influencersmentioning

confidence: 99%

“…Among these, the most relevant study to our research problem is Identification of influencers in complex networks, i.e. [42]. But since [42] focuses on local information dimensionality, rather than turnover intention of influencer employees studied in this paper, it cannot be used for turnover studies of organizational networks.…”

Section: ) Influencersmentioning

confidence: 99%

Know Your Stars Before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique

Younis¹,

Ahsan

2021

IEEE Access

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“…Entropy plays an important role in wide range 66 . Shannon has first proposed Shannon entropy in information theory 67 .…”

Section: Entropy Of Bpasmentioning

confidence: 99%

A vector and geometry interpretation of basic probability assignment in Dempster‐Shafer theory

Luo

Deng

2020

Int J Intell Syst

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Because of the superiority in dealing with uncertainty expression, Dempster-Shafer theory (D-S theory) is widely used in decision theory. In D-S theory, the basic probability assignment (BPA) is the basis and core. Recently, some researchers represent BPA on a N -dimension frame of discernment (FOD) as 2 N -dimension vector in Descartes coordinate system. This representation treats a BPA as a point in the 2 N -dimensional space. A new vector and geometry interpretation of BPA is proposed in this paper.The BPA on a N -dimension FOD is represented as N -dimension vector with parameters in this method.Then BPA is expressed as subset of N -dimension Cartesian space rather than a point. The proposed method is a new way to represent BPA with vector and geometry. The essence of this method is to convert BPA to probability distribution with parameters.The applications of this representation method in D-S theory have been studied. Based on this method, problems in D-S theory can be solved, which include the fusion of BPAs, the distance between BPAs, the correspondence between BPA and probability, and the entropy of BPAs.basic probability assignment, data fusion, dempster-shafer theory, distance, entropy Dempster-Shafer theory (D-S theory), 1,2 as an important and widely used uncertain reasoning method, has been receiving increasingly attention. 3,4 D-S theory assigns probabilities to the power set of events, so it can effectively deal with uncertainty and unknown problems. Because of the effectiveness in pattern recognition, 5,6 decision making, 7,8,9,10,11,12 reasoning, 13,14,15 and uncertainty analysis, 16,17,18,19 D-S theory has been applied in various fields. 20,21,22,23 In D-S theory, A complete set of incompatible basic hypotheses is called a frame of discernment (FOD), which represents all possible answers to a problem. The degree of trust assigned to each subset of FOD is called the basic probability assignment (BPA). Since the N -dimension FOD has 2 N subsets, some researchers represent the BPA as 2 N -dimension vector in Descartes coordinate system. 24,25 A BPA is a point in the 2 N -dimensional space in this representation method.Based on the idea of converting BPA to probability distribution (PD), a new interpretation of D-S theory is proposed in this paper. The BPA on a N -dimension FOD is represented as N -dimension vector with parameters in this method. Each dimension indicates a hypothesis in FOD. In fact, BPA is represented as a subset of N -dimension space rather than a point because of the variable parameters. In this way, BPAs have an intuitive vector and geometry representation. Based on this representation method, several problems in D-S theory have been studied, which include the fusion of BPAs, the distance between BPAs, the correspondence between BPA and probability, and the entropy of BPAs. The example shows that our method provides a new way to solve these basic problems of D-S theory, and can get reasonable results.Combination of BPAs from different information sources is a major problem in D...

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Identification of influencers in complex networks by local information dimensionality

Cited by 107 publications

References 46 publications

Social network analysis of COVID-19 transmission in Karnataka, India

Social network analysis of COVID-19 transmission in Karnataka, India

Know Your Stars Before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique

A vector and geometry interpretation of basic probability assignment in Dempster‐Shafer theory

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