基于双视角网络嵌入聚类集成社区发现算法

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基于双视角网络嵌入聚类集成社区发现算法

Community detection algorithm based on dual-view network embedded clustering integration

基于双视角网络嵌入聚类集成社区发现算法

Community detection algorithm based on dual-view network embedded clustering integration

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doi:10.6040/j.issn.1672-3961.0.2024.191

山东大学学报 (工学版) ›› 2025, Vol. 55 ›› Issue (1): 41-50.doi: 10.6040/j.issn.1672-3961.0.2024.191

• 机器学习与数据挖掘 • 上一篇

王英楠¹,郑文萍^2,3*,杨贵²

1.山西医科大学汾阳学院卫生信息管理系, 山西汾阳 032200;2.山西大学计算机与信息技术学院, 山西太原 030006;3.计算智能与中文信息处理教育部重点实验室(山西大学), 山西太原 030006

发布日期:2025-02-20
作者简介:王英楠(1995— ),男,山西大同人,助教,硕士,主要研究方向为图神经网络、聚类分析. E-mai:1291533544@qq.com. *通信作者简介:郑文萍(1979— ),女,山西榆次人,教授,博士生导师,博士,主要研究方向为复杂网络分析、生物信息学、聚类分析. E-mail: wpzheng@sxu.edu.cn
基金资助:
国家自然科学基金资助项目(62072292);山西省1331工程资助项目

WANG Yingnan¹, ZHENG Wenping^2,3*, YANG Gui²

1. Fenyang College of Shanxi Medical University, Fenyang 032200, Shanxi, China;
2. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China;
3. Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, Shanxi, China

Published:2025-02-20

摘要： 针对现有网络嵌入方法忽略高阶结构,嵌入过程与社区发现任务独立进行,影响社区发现质量的问题,提出基于双视角网络嵌入聚类集成社区发现算法(community detection algorithm based on dual-view network embedded clustering integration, DNECI),算法包括双视角网络嵌入和聚类集成两部分。双视角网络嵌入模块对网络属性信息与拓扑信息实现自适应融合,保留网络属性信息与拓扑的高阶结构。聚类集成模块包括模块度优化和聚类优化两个组件,模块度优化组件利用高阶拓扑结构得到具有最优模块度的社区结果;聚类优化组件通过自监督聚类方法在嵌入空间得到聚类结果;引入互监督机制使两种视角的社区发现结果具有一致性。在4个真实数据集与15个算法进行对比试验,结果表明,DNECI在准确率和标准互信息至少比最先进的基准算法平均提高2.5%和1.4%,在调整兰德系数和F1分数至少平均提高3.7%和1.7%,具有较好的社区发现效果。

关键词: 社区发现, 网络嵌入, 模块度, 自监督, 高阶结构

中图分类号:

TP391

王英楠,郑文萍,杨贵. 基于双视角网络嵌入聚类集成社区发现算法[J]. 山东大学学报 (工学版), 2025, 55(1): 41-50.

WANG Yingnan, ZHENG Wenping, YANG Gui. Community detection algorithm based on dual-view network embedded clustering integration[J]. Journal of Shandong University(Engineering Science), 2025, 55(1): 41-50.

[1] BLONDEL V D, GUILLAUME J, LAMBIOTTE R. Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10):10008.
[2] NEWMAN M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E, 2004, 69(6):66133.
[3] CLAUSET A, NEWMAN M E J, MOORE C. Finding community structure in very large networks[J]. Physical Review E, 2004, 70(2): 66111.
[4] RAGHAVAN U N, ALBERT R, KUMARA S. Near linear time algorithm to detect community structures in large-scale networks[J]. Physical Review E, 2007, 76(3): 36106.
[5] BARBER M J, CLARK J W. Detecting network communities by propagating labels under constraints[J].Physical Review E, 2009, 80(2): 26129.
[6] BELKIN M, NIYOGI P. Laplacian eigenmaps and spectral techniques for embedding and clustering[J]. Advances in Neural Information Processing Systems, 2001, 14(6): 585-591.
[7] TENENBAUM J B, DE SILVA V, LANGGORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
[8] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000(290): 2323-2326.
[9] PEROZZI B, AL-RFOU R,SKIENA S. DeepWalk: online learning of social representations[C] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 701-710.
[10] GROVER A, LESKOVEC J. Node2vec: scalable feature learning for networks[C] // Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2016: 855-864.
[11] WANG D X, CUI P, ZHU WW. Structural deep network embedding[C] // Proceedings of 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2016: 1225-1234.
[12] CAO SS, LU W, XU Q K. Deep neural networks for learning graph representations[C] // Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2016: 1145-1152.
[13] WANG Xiao, CUI Peng, WANG Jing, et al. Community preserving network embedding[C] // Proceedings of the 32th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI press, 2017: 203-209.
[14] YANG C, LIU Z Y, ZHAO D L. Network representation learning with rich text information[C] // Proceedings of the 24th International Joint Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 2111-2117.
[15] HUANG X, LI J D, HU X. Accelerated attributed network embedding[C] // Proceedings of the 2017 SIAM International Conference on Data Mining. Philadelphia, USA: SIAM, 2017: 633-641.
[16] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C] // International Conference on Learning Representations. Toulon, France: ICLR, 2016: 718-725.
[17] VELICKOVIC P, CUCURULL G, GASANOVA A, et al. Graph attention networks[C] //Proceedings of the Int Conf on Learning Representations. Vancouver, Canada: ICLR, 2018: 485-497.
[18] KIPF T N, WELLING M. Variational graph auto-encoders[C] // Proceedings of the NIPS Workshopon Bayesian Deep Learning. Barcelona, Spain: NIPS, 2016: 1611-1616.
[19] SALEHI A, DAVULCU H. Graph attention auto-encoders [C] // Proceedings of the 32nd IEEE International Conference on Tools with Artificial Intelligence. Piscataway, USA: IEEE, 2020: 989-996.
[20] XIE J Y, GIRSHICK R, FARHADI A. Unsupervised deep embedding for clustering analysis[C] // Proceedings of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016: 478-487.
[21] WANG C, PAN S R, HU R Q, et al. Attributed graph clustering: a deep attentional embedding approach[C] // Proceedings of 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 2019: 3670-3676.
[22] BO D Y, WANG X, SHI C, et al. Structural deep clustering network[C] // Proceedings of the 29th International World Wide Web Conference. New York, USA: ACM, 2020: 1400-1410.
[23] 郑文萍,王英楠,杨贵.基于双监督网络嵌入的社区发现算法[J]. 模式识别与人工智能, 2022, 35(3): 283-290. ZHENG Wenping, WANG Yingnan, YANG Gui. Dual supervised network embedding based community detection algorithm[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(3): 283-290.
[24] PAN Shirui, HU Ruiqi, JIANG Jing, et al. Adversarially regularized graph auto-encoder for graph embedding[C] // Proceedings of the 27th International Joint Conference on Artificial Intelligence. Melbourne, Australia: IJCAI, 2018: 2609-2615.

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