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Link prediction based on depth structure in social networks

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Abstract

Link prediction is an important task in social network analysis. Considering that the properties of nodes in social networks are generally inaccurate, it is more reliable and effective to use the network structure features to predict the links in the network. However, a central challenge of such methods is how to fully mine and utilize the network structure information. Here, we introduce a deep structure link prediction model (DSLP), whose idea is to integrate multiple types of community structures and multiple topology features into one probability model. We detect three types of community structures, disjoint, crisp overlap and fuzzy overlap, and then design an edge probability parameter to reflect their importance. Additionally, we propose an effective method to aggregate multiple topology features based on nodes and paths. We perform extensive experiments on artificial networks and real-world social networks to compare the proposed method with nine baseline algorithms, and the results show that our method offers higher precision than that of these well-known approaches. Finally, we discuss the method of integrating trusted node properties and feature selection.

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Data availability

The data that support the findings of this study are available in Stanford Large Network Dataset Collection at http://snap.stanford.edu/data/, reference number [42]. These data were derived from the following resources available in the public domain: Epinions: http://snap.stanford.edu/data/soc-Epinions1.html; GrQc: http://snap.stanford.edu/data/ca-GrQc.html; FFI: http://snap.stanford.edu/data/comm-f2f-Resistance.html; Eu: http://snap.stanford.edu/data/email-Eu-core.html; LastFM: http://snap.stanford.edu/data/feather-lastfm-social.html; Facebook: http://snap.stanford.edu/data/ego-Facebook.html.

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Acknowledgements

This research is supported by the innovative talents project for doctoral students of Chongqing University of Posts and Telecommunications (BYJS201813).

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Jie Yang

  2. Key Laboratory of Web Intelligence and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Yu Wu

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  1. Jie Yang
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Jie yang: formal analysis, data curation, validation, writing—review and editing. Yu Wu: conceptualization, methodology, resources, supervision.

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Correspondence to Yu Wu.

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Yang, J., Wu, Y. Link prediction based on depth structure in social networks. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02178-4

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