Titlebook: Complex Networks & Their Applications IX; Volume 1, Proceeding Rosa M. Benito,Chantal Cherifi,Marta Sales-Pardo Conference proceedings 2021

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Core Method for Community Detectiony the authors, which allows the operator to carry out the procedures required for the method, visualize the results and export the obtained data, are presented. The third part shows the application of the “core method” on a weighted graph, based on the data about the coverage of the activities of th

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Community Detection in a Multi-layer Network Over Social Mediacebook page. The study also investigates how strong the ties between users and their polarity towards the page over the span of time. The results successfully remove the isolates from the network and built a well-defined structure of the community.

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Using Preference Intensity for Detecting Network Communitiese can say that the preference is stronger when . is greater than 0.5, and a value of . between 0.20 and 0.80. The third parameter ., which controls the intensity of community membership, defines the degree of relationship of a node to a community. The communities detected by the preference implicati

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Local Community Detection Algorithm with Self-defining Source Nodesers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains more over networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiment

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Investigating Centrality Measures in Social Networks with Community Structure, and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary

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Efficient Community Detection by?Exploiting Structural Properties of?Real-World User-Item Graphsn a user and an item. Instead of developing a generic clustering algorithm for arbitrary graphs, we tailor our algorithm for user-item graphs by taking advantage of the inherent structural properties that exist in real-world networks. Assuming the existence of the core-periphery structure that has b