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MGRL: attributed multiplex heterogeneous network representation learning based on multi-granularity information fusion

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Abstract

Nowadays, attributed multiplex heterogeneous network (AMHN) representation learning has shown superiority in many network analysis tasks due to its ability to preserve both the structure of the network and the semantics of the nodes. However, few people consider the correlation between content attributes within each node. No personalized analysis model is designed for different semantics of heterogeneous relations. To address these issues, this paper proposes an MGRL model. MGRL adopts a filter based on variance discrimination to filter out the noise information in the node content attributes. To better utilize semantic characteristics of heterogeneous relations, personalized fusion models are designed according to heterogeneous relation categories: peer relations and subordinate relations. Results of experiments conducted on three real-world datasets show an obvious advantage of the proposed MGRL model over state-of-the-art baseline methods.

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Notes

  1. https://dblp.uni-trier.de.

  2. Macro-F1 and Micro-F1 are adopted as evaluation metrics on node classification.

  3. LeakyReLU denotes leaky version of a rectified linear unit.

  4. https://aminer.org/data.

  5. http://www.yelp.com.

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Acknowledgements

The authors thank the National Key Research and Development Program of China under Grant 2017YFC0804002, the National Natural Science Foundation of P. R. China under Grant Nos. 61936001, 61772096 and 61806031, the Natural Science Foundation of Chongqing Nos. cstc2019jcyj-cxttX0002 and cstc2019jcyj-msxmX0485, Graduate Research and Innovation Project Plan of Chongqing Municipal Education Commission (Grant no. CYB18174), the Doctor Training Program of Chongqing University of Posts and Telecommunications (Grant no. BYJS201809) for their support.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China

    Ke Chen, Guoyin Wang, Shun Fu, Jun Hu & Li Liu

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  1. Ke Chen
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  2. Guoyin Wang
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  3. Shun Fu
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  4. Jun Hu
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  5. Li Liu
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Correspondence to Guoyin Wang.

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Chen, K., Wang, G., Fu, S. et al. MGRL: attributed multiplex heterogeneous network representation learning based on multi-granularity information fusion. Int. J. Mach. Learn. & Cyber. 13, 1891–1906 (2022). https://doi.org/10.1007/s13042-021-01494-3

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