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Actively learning to infer social ties

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Abstract

We study the extent to which social ties between people can be inferred in large social network, in particular via active user interactions. In most online social networks, relationships are lack of meaning labels (e.g., “colleague” and “intimate friends”) due to various reasons. Understanding the formation of different types of social relationships can provide us insights into the micro-level dynamics of the social network. In this work, we precisely define the problem of inferring social ties and propose a Partially-Labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social relationships. The model formalizes the problem of inferring social ties into a flexible semi-supervised framework. We test the model on three different genres of data sets and demonstrate its effectiveness. We further study how to leverage user interactions to help improve the inferring accuracy. Two active learning algorithms are proposed to actively select relationships to query users for their labels. Experimental results show that with only a few user corrections, the accuracy of inferring social ties can be significantly improved. Finally, to scale the model to handle real large networks, a distributed learning algorithm has been developed.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Honglei Zhuang, Jie Tang & Wenbin Tang

  2. Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China

    Tiancheng Lou

  3. Nokia Research Center, Beijing, China

    Alvin Chin & Xia Wang

Authors
  1. Honglei Zhuang

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  2. Jie Tang

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  3. Wenbin Tang

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  4. Tiancheng Lou

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  5. Alvin Chin

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  6. Xia Wang

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Corresponding author

Correspondence toJie Tang.

Additional information

Responsible editor: Dimitrios Gunopulos, Donato Malerba and Michalis Vazirgiannis.

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