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1.
Density-based entropy centrality for community detection in complex networks
Krista Rizman Žalik, Mitja Žalik, 2023, original scientific article

Abstract: 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.
Keywords: networks, undirected graphs, community detection, node centrality, label propagation
Published in DKUM: 06.02.2024; Views: 258; Downloads: 19
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2.
Toward the discovery of citation cartels in citation networks
Iztok Fister, Iztok Fister, Matjaž Perc, 2016, original scientific article

Abstract: In this perspective, our goal is to present and elucidate a thus far largely overlooked problem that is arising in scientific publishing, namely the identification and discovery of citation cartels in citation networks. Taking from the well-known definition of a community in the realm of network science, namely that people within a community share significantly more links with each other as they do outside of this community, we propose that citation cartels are defined as groups of authors that cite each other disproportionately more than they do other groups of authors that work on the same subject. Evidently, the identification of citation cartels is somewhat different, although similar to the identification of communities in networks. We systematically expose the problem, provide theoretical examples, and outline an algorithmic guide on how to approach the subject.
Keywords: citation network, citation cartel, network science, community detection, cooperation
Published in DKUM: 10.07.2017; Views: 1771; Downloads: 390
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