Debiasing Community Detection: The Importance of Lowly Connected Nodes

Community detection is an important task in social network analysis, allowing us to identify and understand the communities within the social structures provided by the network. However, many community detection approaches either fail to assign low-degree (or lowly connected) users to communities, or assign them to trivially small communities that prevent them from being included in analysis. In this work we investigate how excluding these users can bias analysis results. We then introduce an approach that is more inclusive for lowly connected users by incorporating them into larger groups. Experiments show that our approach outperforms the existing state-of-the-art in terms of F1 and Jaccard similarity scores while reducing the bias towards low-degree users.

• Publication year

  2019

• Venue

  International Conference on Advances in Social Networks Analysis and Mining

• Publication date

  2019-03-19

• Fields of study

  Physics, Computer Science

• Identifiers
  DOI 10.1145/3341161.3342915arXiv 1903.08136
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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