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Signed directed attention network

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Abstract

Network embedding has facilitated lots of network analytical tasks by representing nodes as low-dimensional vectors. As an extension of convolutional neural networks from Euclidean data to irregular data, Graph Convolutional Networks (GCNs) provide a novel way to learn network representations and have attracted widespread attention currently. Most of the existing GCNs are only applied to unsigned networks. However, networks could have both positive and negative links in the real world, to which the unsigned algorithms are no longer applicable. In this paper, we propose a novel Signed Directed Attention Network model to capture the structural and social theoretical information of signed directed networks comprehensively through an auto-encoder framework. In the encoding block, the information of sign, direction, social theory, and “bridge” edges are encoded into node embeddings by a node fine-grained classification aggregation layer. Besides, a direction parameterization layer is also introduced to convert directions into direction-specific convolutional filters to enhance the node embeddings. In the decoding block, loss functions are designed to model sign, direction, “bridge” edge, and social theory information accordingly and make them complementary to each other to capture the network information fully. Experimental results for the signed link prediction task on several real-world signed directed graphs show that the proposed framework can achieve state-of-the-art performance.

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Notes

  1. http://snap.stanford.edu/data/soc-sign-bitcoin-alpha.html.

  2. http://snap.stanford.edu/data/soc-sign-Slashdot090221.html.

  3. http://snap.stanford.edu/data/soc-sign-epinions.html.

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Acknowledgements

This work was supported in part by the National Social Science Fund of China (20AZD114); Open Research Fund of the Public Security Behavioral Science Laboratory of People’s Public Security University of China (2020SYS08); CCF-Nsfocus “Kunpeng” Research Fund (2020011); and Special Funds for Fundamental Scientific Research Operation Fees of Central Universities (2020JKF310).

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Authors and Affiliations

  1. School of Information Technology and Cyber Security, People’s Public Security University of China, Beijing, 10038, China

    Yong Wu & Binjun Wang

  2. Department of Cyber Security, Henan Police College, Zhengzhou, 450000, China

    Yong Wu

  3. School of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Wei Li

  4. XingYun Lab of NSFOCUS Inc., Beijing, 100089, China

    Wenmao Liu

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  1. Yong Wu
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Correspondence to Yong Wu.

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Wu, Y., Wang, B., Li, W. et al. Signed directed attention network. Computing 105, 1647–1671 (2023). https://doi.org/10.1007/s00607-023-01158-w

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