Debiasing community detection: the importance of lowly connected nodes

@article{Mehrabi2019DebiasingCD,
  title={Debiasing community detection: the importance of lowly connected nodes},
  author={Ninareh Mehrabi and Fred Morstatter and Nanyun Peng and Aram Galstyan},
  journal={Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  year={2019}
}
  • Ninareh Mehrabi, Fred Morstatter, +1 author Aram Galstyan
  • Published in ASONAM '19 2019
  • Computer Science, Physics
  • 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… CONTINUE READING

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    SHOWING 1-2 OF 2 REFERENCES

    Fast unfolding of communities in large networks

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Community Detection in Networks with Node Attributes

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL