Computer Science ›› 2021, Vol. 48 ›› Issue (9): 68-76.doi: 10.11896/jsjkx.210500203

Special Issue: Intelligent Data Governance Technologies and Systems

• Intelligent Data Governance Technologies and Systems • Previous Articles     Next Articles

Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion

ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,ChinaCollaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 211106,China
  • Received:2021-05-28 Revised:2021-06-24 Online:2021-09-15 Published:2021-09-10
  • About author:ZHENG Su-su,born in 1993,postgra-duate.Her main research interests include network representation,data mi-ning and complex network analysis.
    YUAN Wei-wei,born in 1981,Ph.D,associate professor.Her main research interests include machine learning,pattern recognition,social computing and recommender systems.
  • Supported by:
    Key Research and Development Program of Jiangsu Province(BE2019012) and Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China(U2033202).

Abstract: Heterogeneous information network (HIN) embedding maps complex heterogeneous information to a low-dimensional dense vector space,which is conducive to the calculation and storage of network data.Most existing multi-view-based HIN embedding methods consider multiple semantic relationships between nodes,but ignore the incompleteness of the view.Most of views are incomplete and directly fusing multiple incomplete views will affect the performances of the embedding model.To address this problem,we propose a novel HIN embedding model with incomplete multi-view fusion,named IMHE.The key idea of IMHE is to aggregate neighbors of other views to reconstruct the incomplete views.Since different views describe the same HIN,neighbors in other views can restore the structure information of the missing nodes.The IMHE model first generates nodes sequences in different views,and leverages the multi-head self-attention method to obtain single-view embedding.For each incomplete view,IMHE finds the k-order neighbors of the missing nodes in other views,then aggregates the embeddings of neighbors in the incomplete view to generate new embeddings for missing nodes.IMHE finally uses the multi-view canonical correlation analysis method to obtain the joint embedding of nodes,thereby simultaneously extracting the hidden semantic relationship of multiple views.Experiment results on three real-world datasets show that the proposed method is superior to the state-of-the-art methods.

Key words: Heterogeneous information network, Incomplete view, Multi-view fusion, Network embedding

CLC Number: 

  • TP391
[1]DONG Y,HU Z,WANG K,et al.Heterogeneous network representation learning[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence.2020:4861-4867.
[2]WANG X,BO D,SHI C,et al.A Survey on HeterogeneousGraph Embedding:Methods,Techniques,Applications and Sources[J].arXi:2011.14867,2020.
[3]DENG H,HAN J,ZHAO B,et al.Probabilistic topic modelswith biased propagation on heterogeneous information networks[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2011:1271-1279.
[4]LI Z,JIANG J Y,SUN Y,et al.Personalized question routing via heterogeneous network embedding[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019,33:192-199.
[5]HU X T,SHA C F,LIU Y J.Post-processing Network Embedding Algorithm with Random Projection and Principal Component Analysis[J].Computer Science,2021,48(5):124-129.
[6]ZHAO K,BAI T,WU B,et al.Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding[C]//Proceedings of The Web Conference 2020.2020:508-518.
[7]YANG D J,WANG S Z,LI C Z,et al.From Properties to Links:Deep Network Embedding on Incomplete Graphs[C]//CIKM.2017:367-376.
[8]LIN Y,GOU Y,LIU Z,et al.COMPLETER:Incomplete multi-view clustering via contrastive prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:11174-11183.
[9]HE Y,SONG Y,LI J,et al.HeteSpaceyWalk:a heterogeneous spacey random walk for heterogeneous information network embedding[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:639-648.
[10]DONG Y,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.
[11]HOSSEINI A,CHEN T,WU W,et al.Heteromed:Heteroge-neous information network for medical diagnosis[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:763-772.
[12]HU B,SHI C,ZHAO W X,et al.Leveraging meta-path based context for top-n recommendation with a neural co-attention model[C]//Proceedings of the 24th ACM SIGKDD Internatio-nal Conference on Knowledge Discovery & Data Mining.2018:1531-1540.
[13]FU X,ZHANG J,MENG Z,et al.MAGNN:Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding[C]//Proceedings of The Web Conference 2020.2020:2331-2341.
[14]FU T,LEE W C,LEI Z.Hin2vec:Explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:1797-1806.
[15]SHI R,LIANG T,PENG H,et al.HEAM:Heterogeneous Network Embedding with Automatic Meta-path Construction[C]//International Conference on Knowledge Science,Engineering and Management.Springer,Cham,2020:304-315.
[16]TANG J,QU M,MEI Q.Pte:Predictive text embeddingthrough large-scale heterogeneous text networks[C]//Procee-dings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:1165-1174.
[17]XU L,WEI X,CAO J,et al.Embedding of embedding (EOE) joint embedding for coupled heterogeneous networks[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.2017:741-749.
[18]SHAPIRA T,SHAVITT Y.A Deep Learning Approach for IP Hijack Detection Based on ASN Embedding[C]//Proceedings of the Workshop on Network Meets AI & ML.2020:35-41.
[19]WANG X,JI H,SHI C,et al.Heterogeneous graph attention network[C]//The World Wide Web Conference.2019:2022-2032.
[20]WANG L,GAO C,HUANG C,et al.Embedding heterogeneous networks into hyperbolic space without meta-path[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021.
[21]JIANG J Y,LI Z,JU C J T,et al.MARU:Meta-context Aware Random Walks for Heterogeneous Network Representation Learning[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:575-584.
[22]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].arXiv:1706.03762,2017.
[23]YU W J,DING S F.Conditional Generative Adversarial Network Based on Self-attention Mechanism[J].Computer Science,2021,48(1):241-246.
[24]VOITA E,TALBOT D,MOISEEV F,et al.Analyzing multi-head self-attention:Specialized heads do the heavy lifting,the rest can be pruned[J].arXiv:1905.09418,2019.
[25]BANSAL T,JUAN D C,RAVI S,et al.A2N:attending to neighbors for knowledge graph inference[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.2019:4387-4392.
[26]SUN Z,SARMA P,SETHARES W,et al.Learning relation-ships between text,audio,and video via deep canonical correlation for multimodal language analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:8992-8999.
[27]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.2016:855-864.
[28]TANG J,QU M,WANG M,et al.Line:Large-scale information
network embedding[C]//Proceedings of the 24th International Conference on World Wide Web.2015:1067-1077.
[29]ZHANG H,QIU L,YI L,et al.Scalable Multiplex NetworkEmbedding[C]//IJCAI.2018,18:3082-3088.
[30]YUAN C,YANG H.Research on K-value selection method of K-means clustering algorithm[J].J-Multidisciplinary Scientific Journal,2019,2(2):226-235.
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