Cooperation learning from multiple social networks: consistent and complementary perspectives

Weili Guan, Xuemeng Song, Tian Gan, Junyu Lin, Xiaojun Chang, Liqiang Nie

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


GWI survey1 has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively. Indeed, users tend to express their views in more than one social media site. Hence, merging social signals of the same user across different social networks together, if available, can facilitate the downstream analyses. Previous work has paid little attention on modeling the cooperation among the following factors when fusing data from multiple social networks: 1) as data from different sources characterizes the characteristics of the same social user, the source consistency merits our attention; 2) due to their different functional emphases, some aspects of the same user captured by different social networks can be just complementary and results in the source complementarity; and 3) different sources can contribute differently to the user characterization and hence lead to the different source confidence. Toward this end, we propose a novel unified model, which co-regularizes source consistency, complementarity, and confidence to boost the learning performance with multiple social networks. In addition, we derived its theoretical solution and verified the model with the real-world application of user interest inference. Extensive experiments over several state-of-the-art competitors have justified the superiority of our model.1

Original languageEnglish
Pages (from-to)4501-4514
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number9
Publication statusPublished - Sept 2021


  • Cooperation learning
  • multiple social networks
  • source complementarity
  • source consistency

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