Abstract

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.

Date

August 27, 2019

Authors

Ninareh Mehrabi, Fred Morstatter, Nanyun Peng, Aram Galstyan

Book

Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining

Pages

509-512