基于邻域相似的层次粒化的网络表示学习方法

doi:10.16451/j.cnki.issn1003-6059.201906003

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摘要

捕获更多的结构特征给网络表示学习方法带来较高的复杂度.基于分层递阶思想,文中提出基于邻域相似的层次粒化的网络表示学习方法,降低已有网络表示学习方法的复杂度.首先利用节点邻域相似性将网络逐步压缩至粗粒度的表示空间中.然后利用已有的网络表示学习方法学习粗粒的特征表示.最后利用图卷积网络将已学习的粗粒特征逐步细化为原始网络的节点表示.在多个数据集上的实验表明,文中方法可以快速有效大幅压缩网络,降低算法的运行时间.针对节点分类和链接预测任务,当粒化层次较低时,文中方法可以较大幅度提升原有算法的性能.

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	钱峰
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	刘峰

关键词 ：网络表示学习, 分层递阶, 层次粒化, 图卷积网络

Abstract：

The acquisition of structural features brings higher complexity to network representation learning. Based on the idea of hierarchy, an effective method is proposed to reduce the complexity of existing network representation learning methods. The network is gradually compressed into a coarse-grained representation space via node neighborhood similarity. And the coarse-grained feature representation is learned by the existing network representation learning methods. Finally, the learned coarse-grained features are gradually refined into the node representation of the original network using the graph convolution network model. Experimental results on several datasets show that the proposed method compresses the network efficiently and quickly, and the running time of the existing algorithms is greatly reduced. For the task of node classification and link prediction, the proposed method can greatly improve the performance of the original algorithm while the granularity level is low.

Key words： Network Representation Learning Hierarchy Hierarchical Granulation Graph Convolution Network

收稿日期: 2019-05-10

ZTFLH:

TN 929.12

基金资助:

国家重点研究与发展项目(No.2017YFB1401903)、国家自然科学基金项目(No.61876001,61602003,61673020)、安徽省自然科学基金项目(No.1508085MF113,1708085QF156)资助

作者简介: 钱峰,硕士,讲师,主要研究方向为数据挖掘、网络表示学习.E-mail:leslie_chin@163.com.张蕾,硕士,讲师,主要研究方向为数据挖掘、网络表示学习.E-mail:penguinzl@qq.com.赵姝(通讯作者),博士,教授,主要研究方向为机器学习、社交网络、粒计算.E-mail:zhaoshuzs2002@hotmail.com.陈洁,博士,副教授,主要研究方向为智能计算、机器学习、三支决策.E-mail:chenjie200398@163.com.张燕平,博士,教授,主要研究方向为粒计算、机器学习、商空间理论.E-mail:zhangyp2@gmail.com.刘峰,硕士,讲师,主要研究方向为粒计算、商空间理论.E-mail:91051@ahu.edu.cn.

引用本文:

钱峰, 张蕾, 赵姝, 陈洁, 张燕平, 刘峰. 基于邻域相似的层次粒化的网络表示学习方法[J]. 模式识别与人工智能, 2019, 32(6): 504-514. QIAN Feng, ZHANG Lei, ZHAO Shu, CHEN Jie, ZHANG Yanping, LIU Feng. Network Representation Learning Method Based on Hierarchical Granulation Using Neighborhood Similarity. , 2019, 32(6): 504-514.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201906003 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2019/V32/I6/504

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