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CANE: community-aware network embedding via adversarial training

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Abstract

Network embedding aims to learn a low-dimensional representation vector for each node while preserving the inherent structural properties of the network, which could benefit various downstream mining tasks such as link prediction and node classification. Most existing works can be considered as generative models that approximate the underlying node connectivity distribution in the network, or as discriminate models that predict edge existence under a specific discriminative task. Although several recent works try to unify the two types of models with adversarial learning to improve the performance, they only consider the local pairwise connectivity between nodes. Higher-order structural information such as communities, which essentially reflects the global topology structure of the network, is largely ignored. To this end, we propose a novel framework called CANE to simultaneously learn the node representations and identify the network communities. The two tasks are integrated and mutually reinforce each other under a novel adversarial learning framework. Specifically, with the detected communities, CANE jointly minimizes the pairwise connectivity loss and the community assignment error to improve node representation learning. In turn, the learned node representations provide high-quality features to facilitate community detection. Experimental results on multiple real datasets demonstrate that CANE achieves substantial performance gains over state-of-the-art baselines in various applications including link prediction, node classification, recommendation, network visualization, and community detection.

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Notes

  1. https://snap.stanford.edu/data/ca-AstroPh.html.

  2. https://snap.stanford.edu/data/ca-GrQc.html.

  3. http://socialcomputing.asu.edu/datasets/BlogCatalog.

  4. http://www.mattmahoney.net/dc/textdata.

  5. https://grouplens.org/datasets/movielens/100k/.

  6. https://transtats.bts.gov/.

  7. http://socialcomputing.asu.edu/datasets/Twitter.

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Acknowledgements

This work was supported by Guangdong Key Area R & D Plan (Grant No. 2020B010164002), HK RGC Collaborative Research Fund (RGC No. C5026-18G), and the Fundamental Research Funds for the Central Universities (No. NZ2020014).

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Authors and Affiliations

  1. The Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Jia Wang, Jiannong Cao, Wei Li & Senzhang Wang

  2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, People’s Republic of China

    Wei Li

  3. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, Jiangsu, People’s Republic of China

    Wei Li

  4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Senzhang Wang

  5. Collaboration Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Senzhang Wang

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  1. Jia Wang
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  2. Jiannong Cao
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  4. Senzhang Wang
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Correspondence to Senzhang Wang.

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Wang, J., Cao, J., Li, W. et al. CANE: community-aware network embedding via adversarial training. Knowl Inf Syst 63, 411–438 (2021). https://doi.org/10.1007/s10115-020-01521-9

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