Community Detection in Partially Observable Social Networks

Cong Tran, Won Yong Shin, Andreas Spitz

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges. In this article, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an inferred Kronecker generative parameter matrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks.

Original languageEnglish
Article number22
JournalACM Transactions on Knowledge Discovery from Data
Volume16
Issue number2
DOIs
Publication statusPublished - 2022 Apr

Bibliographical note

Publisher Copyright:
© 2021 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

  • General Computer Science

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