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A New Metric to Evaluate Communities in Social Networks Using Geodesic Distance

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Computational Data and Social Networks (CSoNet 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12575))

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Community detection problem is a well-studied problem in social networks. A good community can be defined as a group of nodes that are highly connected with each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. In order to detect local communities, researchers mostly utilize an evaluation metric along with an algorithm to explore local communities. In this paper, the weaknesses of some well-known metrics are considered and a new metric to evaluate the quality of a community, only using local information, is proposed by using geodesic distance. The proposed metric can make a reasonable trade-off between the number of external edges and the density of the community. Furthermore, the experimental results of this study demonstrate that this metric could be useful in terms of evaluating the communities of real social networks.

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Correspondence to Sahar Bakhtar , Mohammad Saber Gholami or Hovhannes A. Harutyunyan .

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Bakhtar, S., Gholami, M.S., Harutyunyan, H.A. (2020). A New Metric to Evaluate Communities in Social Networks Using Geodesic Distance. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham.

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  • Print ISBN: 978-3-030-66045-1

  • Online ISBN: 978-3-030-66046-8

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