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RED: Learning the role embedding in networks via Discrete-time quantum walk

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Abstract

Role embedding aims to embed role-similar nodes into similar representations. Role embedding is significant in graph mining, providing a key bridge between traditional role analysis and machine learning. However, current methods suffer from information loss due to the inherent drawbacks, thus failing to capture role information comprehensively from both global and local perspectives. This paper proposes RED (Role Embedding via Discrete-time quantum walk) to address the above issue via quantum walks, whose characters are naturally applicable to role embedding. Based on the superposition, RED simultaneously learns global role representations by evolving features in a global evolution. Besides, RED uses the quasi-periodicity to capture long-term evolving features within steps. To represent local role information, RED simulates a wave-like diffusion by biased walks, where it learns the closeness from accumulated probabilities for local role representations. To the best of our knowledge, RED is the first to apply quantum walks to the role embedding. Substantial experiments demonstrate that RED significantly outperforms state-of-the-art methods by up to 2300.00% in role detection, 90.93% in equivalency identification, and is overwhelmingly superior in robustness.

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Notes

  1. Node2vec is available on https://github.com/aditya-grover/node2vec, Rolx is available on https://github.com/dkaslovsky/GraphRole, Role2vec is available on https://github.com/benedekrozemberczki/role2vec, and GraphWave is available on https://github.com/benedekrozemberczki/GraphWaveMachine.

  2. https://github.com/Dywangxin/RED_experiments

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Acknowledgements

This work is supported by National High-level Personnel for Defense Technology Program (2017-JCJQ-ZQ-013), NSF 61902405, NSF 62002371, and the China Scholarship Council (CSC Student ID 201903170136). This work is partially done during my research visit to School of Computing, National University of Singapore, under the supervision of Prof. Xiaokui XIAO.

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    1. College of Computer Science, National University of Defense Technology, Changsha, China

      Xin Wang, Songlei Jian, Kai Lu, Yi Zhang & Kai Liu

    2. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China

      Xin Wang, Songlei Jian, Kai Lu, Yi Zhang & Kai Liu

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    1. Xin Wang
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    Correspondence to Xin Wang.

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    Xin Wang and Sonelei Jian are contributed equally to this work.

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    Wang, X., Jian, S., Lu, K. et al. RED: Learning the role embedding in networks via Discrete-time quantum walk. Appl Intell 52, 1493–1507 (2022). https://doi.org/10.1007/s10489-021-02342-1

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