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Link Prediction Based on Hyper-Substructure Network

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Graph Data Mining

Part of the book series: Big Data Management ((BIGDM))

Abstract

Link prediction has long been the focus in the analysis of network-structured data. Though straightforward and efficient, heuristic approaches like Common Neighbors perform link prediction with pre-defined assumptions and only use superficial structural features. While it is widely acknowledged that a node could be characterized by a bunch of neighbor nodes, network embedding algorithms and newly emerged graph neural networks still exploit structural features on the whole network, which may inevitably bring in noises and limits the scalability of those methods. In this chapter, we propose an end-to-end deep learning framework, namely hyper-substructure enhanced link predictor (HELP), for link prediction. HELP utilizes local topological structures from the neighborhood of the given node pairs, avoiding useless features. For further exploiting higher-order structural information, HELP also learns features from hyper-substructure network (HSN). Extensive experiments on five benchmark datasets have shown the state-of-the-art performance of HELP on link prediction.

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Authors and Affiliations

  1. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

    Jian Zhang, Jinyin Chen & Qi Xuan

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Jian Zhang, Jinyin Chen & Qi Xuan

Authors
  1. Jian Zhang
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  2. Jinyin Chen
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  3. Qi Xuan
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Correspondence to Qi Xuan .

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Editors and Affiliations

  1. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

    Qi Xuan

  2. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

    Zhongyuan Ruan

  3. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

    Yong Min

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Zhang, J., Chen, J., Xuan, Q. (2021). Link Prediction Based on Hyper-Substructure Network. In: Xuan, Q., Ruan, Z., Min, Y. (eds) Graph Data Mining. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-16-2609-8_2

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  • DOI: https://doi.org/10.1007/978-981-16-2609-8_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2608-1

  • Online ISBN: 978-981-16-2609-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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