An approach based on the clustering coefficient for the community detection in social networks

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.

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Community Detection in Static and Dynamic Social Networks

2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) ◽

10.1109/icacccn51052.2020.9362903 ◽

2020 ◽

Author(s):

Sunita Tripathi ◽

Rajiv Ranjan Tewari ◽

Sugam Kumar Tripathi

Keyword(s):

Social Networks ◽

Community Detection ◽

Dynamic Social Networks

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Emergence and evolution of social networks through exploration of the Adjacent Possible space

Communications Physics ◽

10.1038/s42005-021-00527-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Enrico Ubaldi ◽

Raffaella Burioni ◽

Vittorio Loreto ◽

Francesca Tria

Keyword(s):

Social Networks ◽

Community Structure ◽

Social Interactions ◽

Clustering Coefficient ◽

Human Beings ◽

Broad Distribution ◽

Phone Calls ◽

Dynamical Features ◽

Twitter Users ◽

American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

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Community Detection in Social Networks using Local Topology and Information Exchange

2021 International Conference on Intelligent Technologies (CONIT) ◽

10.1109/conit51480.2021.9498401 ◽

2021 ◽

Author(s):

Soumita Das ◽

Anupam Biswas

Keyword(s):

Social Networks ◽

Community Detection ◽

Information Exchange ◽

Local Topology

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

Mathematics ◽

10.3390/math9040443 ◽

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.

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