scholarly journals Mutual Community Detection across Multiple Partially Aligned Social Networks

2016 ◽  
Vol 3 (2) ◽  
pp. 47-69 ◽  
Author(s):  
Jiawei Zhang ◽  
Songchang Jin ◽  
Philip S. Yu
Keyword(s):  
Social Networks ◽  
Community Detection

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals Community Detection Problem Based on Polarization Measures: An Application to Twitter: The COVID-19 Case in Spain

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 443
Author(s):  
Inmaculada Gutiérrez ◽  
Juan Antonio Guevara ◽  
Daniel Gómez ◽  
Javier Castro ◽  
Rosa Espínola
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Efficient Algorithm ◽  
Fuzzy Measure ◽  
The Political ◽  
Detection Problem ◽  
Additional Information ◽  
Political Position ◽  
Networks Analysis ◽  
Polarization Capacity

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the JDJpol measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem.

scholarly journals Community detection in multi-relational social networks based on relational concept analysis

2019 ◽  
Vol 159 ◽  
pp. 291-300
Author(s):  
Soumaya Guesmi ◽  
Chiraz Trabelsi ◽  
Chiraz Latiri
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Concept Analysis ◽  
Relational Concept
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