A classification of community detection methods in social networks: a survey

TL;DR: This paper provides a comprehensive overview and taxonomy of local community detection algorithms and gathers the best documented tools and the most commonly used datasets in the local community Detection literature to help researchers identify the tools they can use to prove their methods.

TL;DR: A dynamic spreading model is proposed, namely the susceptible–infected–recovered–susceptible with vaccination and quarantine states (SIRS-QV) to control the speed of malware propagation in communities.

TL;DR: The bourgeoning of Online Social Networks has triggered an increase in undesirable acts caused by some disruptive entities, e.g. fake accounts, bots, and cyber-extremists.

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.

TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.

TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.