Self-supervised hypergraph representation learning for sociological analysis

Xiangguo SUN, Hong CHENG, Bo LIU, Jia LI, Hongyang CHEN, Guandong XU, Hongzhi YIN

Research output: Contribution to journalArticlespeer-review

4 Citations (Scopus)

Abstract

Modern sociology has profoundly uncovered many convincing social criteria for behavioral analysis. Unfortunately, many of them are too subjective to be measured and very challenging to be presented in online social networks (OSNs) for the large data volume and complicated environments to be explored. On the other hand, data mining techniques can better find data patterns but many of them leave behind unnatural understanding to humans. Although there are some works trying to integrate social observations for specific tasks, they are still hard to be applied to more general cases. In this paper, we propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. Our highlights are three-fold: First, we propose an effective hypergraph awareness and a fast line graph construction framework. The hypergraph can more profoundly indicate the interactions between individuals and their environments because each edge in the hypergraph (a.k.a hyperedge) contains more than two nodes, which is perfect to describe social. A line graph treats each social environment as a super node with the underlying influence between different environments. In this way, we go beyond traditional pair-wise relations and explore richer patterns under various sociological criteria; Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users, users to environments, environment to users, and environments to environments. The neural network can be learned via a task-free method, making our model very flexible to support various data mining tasks and sociological analysis; Third, we propose both qualitative and quantitive solutions to effectively evaluate the most common sociological criteria like social conformity, social equivalence, environmental evolving and social polarization. Our extensive experiments show that our framework can better support both data mining tasks for online user behaviors and sociological analysis. Copyright © 2023 IEEE.

Original languageEnglish
Pages (from-to)11860-11871
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number11
Early online dateFeb 2023
DOIs
Publication statusPublished - Nov 2023

Citation

Sun, X., Cheng, H., Liu, B., Li, J., Chen, H., Xu, G., & Yin, H. (2023). Self-supervised hypergraph representation learning for sociological analysis. IEEE Transactions on Knowledge and Data Engineering, 35(11), 11860-11871. https://doi.org/10.1109/TKDE.2023.3235312

Keywords

  • Hypergraph
  • Self-supervised learning
  • Social conformity
  • Social influence

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