[1]ZHANG Lei,QIAN Feng,ZHAO Shu,et al.Network representation learning based on multi-granularity structure[J].CAAI Transactions on Intelligent Systems,2019,14(6):1233-1242.[doi:10.11992/tis.201905045]
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Network representation learning based on multi-granularity structure

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1233-1242 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Network representation learning based on multi-granularity structure
Author(s):
ZHANG Lei1; 2;  QIAN Feng1; 2;  ZHAO Shu1;  CHEN Jie1;  ZHANG Yanping1;  LIU Feng1
1. School of Computer Science and Technology, Anhui University, Hefei 230601, China;
2. School of Mathematics and Computer Science, Tongling University, Tongling 244061, China
Keywords:
network represent learning; network topology; modularity increment; network coarsening; multi-granularity structure; Graph Convolution Network; node classification; link prediction
CLC:
TN929.12
DOI:
10.11992/tis.201905045
Abstract:
The Graph Convolution Network (GCN) can adapt to graphs with different structures. However, most GCN-based models have difficulty effectively capturing the high-order similarity of the network. Simply adding a convolution layer will cause the output features to be too smooth and difficult to distinguish. Moreover, the deep neural network is more difficult to train. In this paper, multi-granularity structure and a GCN are combined to represent the node characteristics of the learning network. A multi-granularity structure-based network representation learning method, Multi-GS, is proposed. First, based on the idea of modularity clustering and granular computing, hierarchical multi-granularity space was used to replace the original single-layer network topology space. The GCN model was then used to learn the representation of granules in different coarse- and fine-granularity spaces. Finally, representations of the different grains were combined into representations of nodes in the original space from coarse to fine. Experimental results showed that multi-GS can capture a variety of structural information, including first-order and second-order similarity, intra-community similarity (high-order structure), and inter-community similarity (global structure). In most cases, using multi-granularity structure can improve the classification performance of node classification tasks.
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