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MGPOOL: multi-granular graph pooling convolutional networks representation learning

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Abstract

Graph convolutional network (GCN) nowadays become new state-of-the-art for networks representation learning. Most of the existing methods are single-granular methods that failed to analyze the graph at multi-granular views so as to lose abundant information. Advanced graph pooling techniques can be successfully benefiting from semi-supervised networks representation learning. How to capture multi-granular information through the graph pooling techniques on graphs without additional input features is a great challenge. Technically speaking, we propose our graph node embeddings framework, MGPOOL. First, inspired by the triadic influence learning, we use the 3-clique algorithm to coarsen the graph repeatedly. Three nodes of a triangle form a supernode. We treat the supernodes as key nodes for our graph pooling operations. That keeps the local relationship. These graphs capture consecutive 3-cliques from the finest to the coarsest to preserve global structural relationships. Second, we use the unsupervised single-granular algorithms on the coarsest graph to acquire its node embeddings. Based on that, our graph pooling operations combining with that node embeddings to generate another same size of the coarsest graph. This makes up for the uniqueness of the coarsening result at a time and expands the receptive field for each node to avoid high-proximity information lost. Third, we take the embeddings, the coarsest graph and new coarsest graph as uniform input of MGPOOL. We restore the coarsest graph to the original graph to get the original graph node embeddings. The experimental results on four public datasets, Wiki, Cora, CiteSeer, and DBLP, demonstrate that our method has a better Macro F1 value for node classification tasks and AUC and Ap value for link prediction than the baseline methods.

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Notes

  1. https://github.com/GTmac/HARP/tree/master/

    example_networks/DBLP.

  2. https://github.com/GTmac/HARP/tree/master/

    example_networks/citeseer.

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Acknowledgements

The authors would like to thank the support by National Natural Science Foundation of China (Grants #61876001, #61602003 and #61673020). Suzhou Scientific and Technological Research Projects (Grants #SZ2018GG04), Provincial Universities Academic Program for Excellent Young Talents At Home and Abroad (Grants #gxgnfx2018052). Key Research Projects of Anhui Provincial Education Department (Grants #KJ2019A1001, #KJ2019A0668), The Open Research Fund of National Engineering Research Center (Grants #AE2019010),The Key Research and Development Projects of Anhui Province (Grants #202004a05020043, #202004a06020045, #202004b11020023).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Hefei, 230601, China

    Zhenghua Xin, Jie Chen & Shu Zhao

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Jie Chen & Shu Zhao

  3. School of Information Engineering, Suzhou University, Suzhou, 234000, China

    Zhenghua Xin, Aidong Fang, Zhenggao Pan & Lin Cui

  4. School of Computer Engineering, Bengbu University, Bengbu, 233000, China

    Guolong Chen

  5. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei, 230601, China

    Jie Chen & Shu Zhao

  6. Beijing Remang Technology Co. LTD, Beijing, 100000, China

    Zongchao Wang

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  1. Zhenghua Xin
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  2. Guolong Chen
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  3. Jie Chen
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  4. Shu Zhao
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Correspondence to Shu Zhao.

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Xin, Z., Chen, G., Chen, J. et al. MGPOOL: multi-granular graph pooling convolutional networks representation learning. Int. J. Mach. Learn. & Cyber. 13, 783–796 (2022). https://doi.org/10.1007/s13042-021-01328-2

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  • DOI: https://doi.org/10.1007/s13042-021-01328-2

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