Social Network Nodes Classification Method Based on Multi-information Fusion

doi:10.3778/j.issn.1673-9418.2206102

Abstract

Abstract: For the problem of poor performance of social network nodes, a model of integrating multiple information graph convolutional networks (IMIGCN) for node classification is proposed. Firstly, using the eigenvector X and the adjacency matrix A, this paper respectively constructs the homogeneous matrix FA containing the homogeneous information between nodes and the co-citation matrix CoA containing the co-citation information. The triangular structure in the network is analyzed, and the triangular matrix TriA containing the triangular information between nodes is constructed by the transformation formula. On this basis, the information of the node itself is integrated. Next, this paper improves the traditional graph convolutional networks (GCN) model. The single kernel of GCN is improved to adaptive multi-kernel, and the results of multi-kernel learning are adaptively fused into one embedding through the attention mechanism, so as to achieve the effect of integrating multiple information at the same time by one convolution. In order to learn more information, the embedding in the model process is designed as multi-head, and the weight assignment of multi-head embedding is adaptively learned through multi-head embedding attention. Experimental results show that, compared with the existing node classification models with better performance, the classification accuracy of the proposed IMIGCN on social networks is improved by 0.0098 to 0.0532, and the F1 index is improved by 0.0127 to 0.0536, which proves that the proposed IMIGCN is reasonable and effective.

Key words: social network, information extraction, information fusion, node classification, graph convolutional network (GCN)

摘要： 针对社交网络节点分类效果不佳的问题，提出一种融合多元信息的图卷积网络节点分类模型（IMIGCN）。首先，利用特征向量X和邻接矩阵A，分别构造包含节点间同质信息的同质矩阵FA和共引信息的共引矩阵CoA；分析网络中的三角结构，通过转换公式构造包含节点间三角信息的三角矩阵TriA。在此基础上，融入节点自身信息。接着，改进传统图卷积网络（GCN）模型。将GCN的单核改进为适应性多核，通过注意力机制将多核学习的结果自适应融合为一个嵌入，达到一次卷积同时融合多元信息的效果。为了学习更多信息，将模型过程中的嵌入设计为多头，通过多头嵌入注意力自适应学习多头嵌入的权重分配。实验结果表明，与现有较优的节点分类模型相比，提出的IMIGCN在社交网络上的分类精确度提高0.009 8~0.053 2，F1指标提升0.012 7~0.053 6，证明了提出的IMIGCN合理有效。

关键词: 社交网络, 信息提取, 信息融合, 节点分类, 图卷积网络（GCN）

LIU Chao, LIANG Anting, LIU Xiaoyang, HUANG Xianying. Social Network Nodes Classification Method Based on Multi-information Fusion[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(9): 2198-2208.

刘超, 梁安婷, 刘小洋, 黄贤英. 融合多元信息的社交网络节点分类方法[J]. 计算机科学与探索, 2023, 17(9): 2198-2208.

References

[1] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J]. arXiv:1609.02907, 2016.
[2] HU F, ZHU Y, WU S, et al. Hierarchical graph convolutional networks for semi-supervised node classification[J]. arXiv: 1902.06667, 2019.
[3] ABU-EL-HAIJA S, PEROZZI B, KAPOOR A, et al. Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing[C]//Proceedings of the 36th International Conference on Machine Learning, Long Beach, Jun 10-15, 2019: 21-29.
[4] LIN G, WANG J, LIAO K, et al. Structure fusion based on graph convolutional networks for node classification in citation networks[J]. Electronics, 2020, 9(3): 432.
[5] XU D, CHENG W, LUO D, et al. Adaptive neural network for node classification in dynamic networks[C]//Proceedings of the 2019 IEEE International Conference on Data Mining, Beijing, Nov 8-11, 2019. Piscataway: IEEE, 2019: 1402-1407.
[6] LI B, PI D. Learning deep neural networks for node classification[J]. Expert Systems with Applications, 2019, 137: 324-334.
[7] LI B, PI D, LIN Y. Learning ladder neural networks for semi-supervised node classification in social network[J]. Expert Systems with Applications, 2021, 165: 113957.
[8] VELIČKOVIĆ P, UCURULL G, CASANOVA A,?et al. Graph attention networks[J]. arXiv:1710.10903, 2017.
[9] WU J, HE J, XU J. DEMO-Net: degree-specific graph neural networks for node and graph classification[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 406-415.
[10] WANG X, ZHU M, BO D,?et al. AM-GCN: adaptive multi- channel graph convolutional networks[C]//Proceedings of the 2020 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Jul 6-10, 2020. New York: ACM, 2020: 1243-1253.
[11] BHAGAT S, CORMODE G, MUTHUKRISHNAN S. Node classification in social networks[M]//Social Network Data Analytics. Berlin, Heidelberg: Springer, 2011.
[12] MCPHERSON M, SMITHLOVIN L, COOK J M. Birds of a feather: homophily in social networks[J]. Annual Review of Sociology, 2001, 27(1): 415-444.
[13] RAQUEL U, FRANCISCO C, GUY M, et al. A social network based approach for consensus achievement in multiperson decision making[J]. Information Fusion, 2019, 47: 72-87.
[14] UMIT C, BILAL A. A new direction in social network analysis: online social network analysis problems and applications[J]. Physic: Statistical Mechanics and Its Applications, 2014, 535: 122372.
[15] GETOOR L. Link-based classification[M]//Advanced Methods for Knowledge Discovery from Complex Data. Berlin, Heidelberg: Springer, 2005: 189-207.
[16] BHAGAT S, ROSENBLUM I, CORMODE G. Applying link-based classification to label blogs[C]//LNCS 5439: Proceedings of the 9th Web KDD and 1st SNA-KDD Workshop on Web Mining and Social Network Analysis, San Jose, Aug 12-15, 2007. Berlin, Heidelberg: Springer, 2007: 97-117.
[17] RON M, SHAI S, SHALEV I, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824-827.
[18] YU J, YIN H, LI J, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]// Proceedings of the 2021 Web Conference, Ljubljana, Apr 19-23, 2021. New York: ACM, 2021: 413-424.
[19] WANG W, LIU X, JIAO P, et al. A unified weakly supervised framework for community detection and semantic matching[C]//LNCS 10939: Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Jun 3-6, 2018. Cham: Springer, 2018: 218-230.
[20] MENG Z, LIANG S, BAO H, et al. Co-embedding attributed networks[C]//Proceedings of the 2019 12th ACM International Conference on Web Search and Data Mining, Melbourne, Feb 11-15, 2019. New York: ACM, 2019: 393-401.
[21] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Dec 5-10, 2016. Red Hook: Curran Associates, 2016: 3837-3845.