计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 146-154.doi: 10.11896/jsjkx.211200082

• 数据库&大数据&数据科学 • 上一篇    下一篇

融合多特征的属性异质网络嵌入方法

汤启友, 张凤荔, 王瑞锦, 王雪婷, 周志远, 韩英军   

  1. 电子科技大学信息与软件工程学院 成都610054
  • 收稿日期:2021-12-07 修回日期:2022-03-28 发布日期:2022-12-14
  • 通讯作者: 张凤荔(fzhang@uestc.edu.cn)
  • 作者简介:(tangqiyou2018@163.com)
  • 基金资助:
    国家自然科学基金(61802033,61472064,61602096);四川省区域创新合作项目(2020YFQ0018);四川省科技计划重点研发项目(2021YFG0027,2020YFG0475,2018GZ0087,2019YJ0543);博士后基金项目(2018M643453);广东省国家重点实验室项目(2017B030314131);网络与数据安全四川省重点实验室开放课题(NDSMS201606)

Method of Attributed Heterogeneous Network Embedding with Multiple Features

TANG Qi-you, ZHANG Feng-li, WANG Rui-jin, WANG Xue-ting, ZHOU Zhi-yuan, HAN Ying-jun   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Received:2021-12-07 Revised:2022-03-28 Published:2022-12-14
  • About author:TANG Qi-you,born in 1996,postgra-duate,is a member of China Computer Federation.His main research interests include data mining and network embedding.ZHANG Feng-li,born in 1963,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include network security,cloud computing and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(61802033,61472064,61602096),Sichuan Regional Innovation Cooperation Project(2020YFQ0018),Sichuan Science and Technology Program(2021YFG0027,2020YFG0475,2018GZ0087,2019YJ0543),Chinese Postdoctoral Science Foundation(2018M643453),Guangdong Provincial Key Laboratory Project(2017B030314131) and Network and Data Security Key Laboratory of Sichuan Province Open Issue(NDSMS201606).

摘要: 网络嵌入旨在用低维、实值的向量表示非结构化网络中的节点,使节点嵌入尽可能地保留原始网络中的结构特征与属性特征。然而,当前研究主要集中于嵌入网络结构,对异质信息网络中具有丰富语义的关系属性和节点属性考虑得较少,可能导致节点嵌入语义缺失,从而影响下游应用的预测效果。针对该问题,设计了一种融合多特征的属性异质网络嵌入(Attributed Heterogeneous Network Embedding with Multiple Features,MFAHNE)方法。该方法通过序列采样、结构特征嵌入、属性特征嵌入、特征融合等步骤将网络中的关系属性、节点属性、结构语义等特征融合至最终节点嵌入。实验结果表明,该方法能兼顾结构特征与属性特征,实现两种特征信息的相互补充,优于传统的网络嵌入方法。

关键词: 网络嵌入, 异质信息网络, 结构特征, 属性特征, 属性异质网络

Abstract: Network embedding aims to represent nodes in unstructured network with low-dimensional,real-valued vectors,so that node embedding can retain the structural and attribute features of the original network as much as possible.However,current research mainly focuses on embedding the network structure.There are few researches considering relationship attributes and node attributes with rich semantics in heterogeneous information networks,which mayresult in semetic loss of node embedding and affect the prediction effect of downstream applications.To solve this problem,this paper designs a method of attributed heteroge-neous network embedding with multiple features(MFAHNE).This method integrates the relationship attributes,node attributes and structural semantics in the network into the final node embedding through the steps of sampling sequence,embedding with structural feature,embedding with attribute feature and merging features.Experiment result shows that this method can take into account the structural feature and attribute features,realizes the mutual supplement of two kinds of feature information,and is better than the traditional network embedding methods.

Key words: Network embedding, Heterogeneous information network, Structural feature, Attribute feature, Attributed heterogeneous network

中图分类号: 

  • TP181
[1]SUN Y Z,HAN J W.Mining heterogeneous information net-works:a structural analysis approach[J].SIGKDD Explorations,2012,14(2):20-28.
[2]WANG Z F,WEN R,CHEN X,et al.Online Disease Diagnosis with Inductive Heterogeneous Graph Convolutional Networks[C]//Proceedings of the 30th International Conference on World Wide Web.2021:3349-3358.
[3]HONG H T,LIN Y C,YANG X Q,et al.HetETA:Heteroge-neous Information Network Embedding for Estimating Time of Arrival[C]//Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2020:2444-2454.
[4]SEBASTIAN Y,SIEW E,ORIMAYE S O.Learning the heterogeneous bibliographic information network for literature-based discovery[J].Knowledge Based Systems,2017,115:66-79.
[5]ZHOU H,ZHAO Z Y,LI C.Survey on Representation Learning Methods Oriented to Heterogeneous Information Network[J].Journal of Frontiers of Computer Science and Technology,2019,13(7):1081-1093.
[6]DING Y,WEI H,PAN Z S,et al.Survey of network representation learning[J].Computer Science,2020,47(9):52-59.
[7]PEROZZI B,AL-RFOU R,SKIENA S.DeepWalk:Online Lear-ning of Social Representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2014:701-710.
[8]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space[C]//Proceedings of the 1st International Conference on Learning Representations.2013.
[9]MIKOLOV T,SUTSKEVER I,CHEN K,et al.DistributedRepresentations of Words and Phrases and their Compositiona-lity[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.2013:3111-3119.
[10]GROVER A,LESKOVEC J.node2vec:Scalable Feature Lear-ning for Networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:855-864.
[11]TANG J,QU M,WANG M Z,et al.LINE:Large-scale Information Network Embedding[C]//Proceedings of the 24th International Conference on World Wide Web.2015:1067-1077.
[12]HAMILTON W,YING Z T,LESKOVEC J.Inductive Repre-sentation Learning on Large Graphs[C]//Advances in Neural Information Processing Systems 30:Annual Conference on Neural Information Processing Systems.2017:1024-1034.
[13]VELICKOVIC P,CUCURULL G,CASANOVA A,et al.Graph Attention Networks[C]//Proceedings of the 6th International Conference on Learning Representations.2018.
[14]DONG Y X,CHAWLA N V,SWAMI A.metapath2vec:Scalable Representation Learning for Heterogeneous Networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:135-144.
[15]FU T Y,LEE W C,LEI Z.HIN2Vec:Explore Meta-paths in Heterogeneous Information Networks for Representation Lear-ning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:1797-1806.
[16]WANG X,JI H Y,SHI C,et al.Heterogeneous Graph Attention Network[C]//The 28th World Wide Web Conference.2019:2022-2032.
[17]LIAO L Z,HE X N,ZHANG H W,et al.Attributed Social Network Embedding[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(12):2257-2270.
[18]BANDYOPADHYAY S,BISWAS A,KARA H,et al.A Multilayered Informative Random Walk for Attributed Social Network Embedding[C]//Proceedings of the 24th European Conference on Artificial Intelligence.2020:1738-1745.
[19]WANG Y Y,DUAN Z H,LIAO B B,et al.Heterogeneous Attributed Network Embedding with Graph Convolutional Networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence.2019:10061-10062.
[20]LE Q,MIKOLOV T.Distributed Representations of Sentences and Documents[C]//Proceedings of the 31th International Conference on Machine Learning.2014:1188-1196.
[21]JOULIN A,GRAVE E,BOJANOWSKI P,et al.Bag of Tricks for Efficient Text Classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.2017:427-431.
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