Abstract
Community detection is one of the main topics of social network analysis, which is attracting increasing attention from many researchers. In fact, the community detection can be done either from the global network (such is the case of global communities), or from some specific nodes: case of ego-communities. The early community detection works focused on network partitioning into several global communities. Over time, researchers have been interested in studying ego-communities to analyze the impact of interest nodes within network. Even if the global community survey is very well covered through works like that of Fortunato; that relating to ego-communities is not yet. The purpose of this paper is to propose a survey on ego-community detection approaches in order to reduce the survey lack while focusing on the strengths and weaknesses of existing solutions.
Keywords
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- 1.
The interest node is also called ego.
- 2.
A graph is simple if it contains neither loops nor multiple edges.
- 3.
These cliques are called \(\alpha \)-Optimal-Quasi-Cliques (\(\alpha \)-OQC).
- 4.
An algorithm is said to be deterministic if it still detects the same communities by running it multiple times on the same dataset.
- 5.
An algorithm is stable if it does not find strongly different communities on two topologically similar graphs.
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Ould Mohamed Moctar, A., Sarr, I. (2019). Survey on Social Ego-Community Detection. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_31
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