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hier2vec: interpretable multi-granular representation learning for hierarchy in social networks

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Abstract

Network representation learning (NRL) maps vertices into latent vector space for further network inference. The existing algorithms concern more about whether the vectors of two similar nodes be close in latent vector space while the hierarchy proximity has been largely neglected by them. The distribution of the representation vectors needs to reflect the hierarchical structural properties which widely exist in networks. In this paper, we propose a novel network representation learning framework that can encode the interpretable hierarchical structural semantics into the representation vectors. Specifically, we measure the distance and importance degree of nodes in the original network and map the nodes to a tree space. This makes the hierarchical structural relations in the original network be clearly revealed by the tree which is also of good interpretability. In this paper, the local structural proximities and the interpretable hierarchy knowledge are encoded into vector space by optimizing the objective function. Extensive experiments conducted on the realistic data sets demonstrate that the proposed approach outperforms the existing state-of-the-art approaches on tasks of node classification, link prediction, and visualization. Finally, a case study is conducted for further analysis about how the proposed model works.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their constructive comments. This work is supported in part by the National Science Foundation of China (grant no. 61936001, 61772096, 61966005), Graduate Research and Innovation Project Plan of Chongqing Municipal Education Commission (grant no. CYB18174) and Doctor Training Program of Chongqing University of Posts and Telecommunications (grant no. BYJS201809).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China

    Shun Fu & Guoyin Wang

  2. School of information engineering, Guizhou University of Engineering Science, Guiyang, Guizhou, China

    Ji Xu

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  1. Shun Fu
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  2. Guoyin Wang
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  3. Ji Xu
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Correspondence to Guoyin Wang.

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Fu, S., Wang, G. & Xu, J. hier2vec: interpretable multi-granular representation learning for hierarchy in social networks. Int. J. Mach. Learn. & Cyber. 12, 2543–2557 (2021). https://doi.org/10.1007/s13042-021-01338-0

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