Centrality Measures in Finding Influential Nodes for the Big-Data Network

Gopalakrishnan, Sathyanarayanan; Sridharan, Supriya; Swaminathan, V.

doi:10.1007/978-3-030-58675-1_103-1

Cited by 4 publications

(6 citation statements)

References 68 publications

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“…Centrality is a concept used in SNA to measure the importance of nodes within a network (Sapini et al, 2022). It quantifies the degree to which a node is positioned at the center or core of the network, indicating its influence, control, or access to the information within the social structure (Gopalakrishnan et al, 2020). In this research, Weighted Degree Centrality (WDC) was employed as a method for analyzing the network model.…”

Section: Bibliometric Analysis Tools and Social Network Analysismentioning

confidence: 99%

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2024

IJELHE

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This paper analyses women's experience of consuming TikTok to cope with everyday stress. Our objectives are to examine the significant meanings of TikTok to women as a coping tool, to explore the motivations behind women's engagement with the platform for stress and emotion management, including to investigate the relationship between women's social and economic conditions and their consumption of TikTok for stress-coping. The investigation is carried out by conducting semi-structured interviews to collect qualitative data. Six women were purposively sampled from a lower-income (B40) background who all use TikTok. Our results indicate several positive outcomes in women's stress and emotion management, particularly in managing work and personal stressors when consuming TikTok. Moreover, the results revealed that TikTok does not necessarily gratifiy as a tool for social validation but rather as a tool to comprehend and cope with women's stress. Additionally, TikTok positively affects women by encouraging them to seek self-betterment through its various media channels of entertainment, information, and distraction. The implications of this paper could be used to better understand the nature of stress that women face in this current era, and how social media is capable of becoming an effective tool for stress-coping.

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Section: Bibliometric Analysis Tools and Social Network Analysismentioning

confidence: 99%

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2024

IJELHE

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“…closeness centrality, 33 harmonic centrality, 34 betweenness centrality, 32 eigenvector centrality, 35 and eccentricity centrality. 36 These metrics provide diverse perspectives for evaluating the importance and influence of nodes within the network.…”

Section: Analysis Of the Centralitymentioning

confidence: 99%

“…Eccentricity centrality is another metric to measure the importance of a node in the graph. 36 It is calculated as the maximum distance of the shortest paths between one node and all the other nodes in the graph. A high eccentricity centrality value indicates that the nodes in the network are close to the specific node.…”

Section: Analysis Of the Centralitymentioning

confidence: 99%

“…In other words, centrality can be used to evaluate the relative importance of a node in the graph, and consequently can be used to find out the most important node in a complex network. Various centrality measurements have been defined, mainly including closeness centrality, 33 harmonic centrality, 34 betweenness centrality, 32 eigenvector centrality, 35 and eccentricity centrality 36 . These metrics provide diverse perspectives for evaluating the importance and influence of nodes within the network.…”

Section: Ripple Network Topology Analysismentioning

confidence: 99%

“…Additionally, a node with a high eigenvector centrality value does not necessarily have a high degree, because a node may have a small number of neighbours but be located in the centre of the graph. Eccentricity centrality is another metric to measure the importance of a node in the graph 36 . It is calculated as the maximum distance of the shortest paths between one node and all the other nodes in the graph.…”

Section: Ripple Network Topology Analysismentioning

confidence: 99%

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The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the data set depend on each other. This paper surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are: identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing.

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Centrality Measures in Finding Influential Nodes for the Big-Data Network

Cited by 4 publications

References 68 publications

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Topology analysis of the Ripple transaction network

Formal Concept Analysis Applications in Bioinformatics

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