计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 68-76.doi: 10.11896/jsjkx.210500203
所属专题: 智能数据治理技术与系统
郑苏苏, 关东海, 袁伟伟
ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei
摘要: 异质信息网络(Heterogeneous Information Network,HIN)嵌入将复杂的异质信息映射到低维稠密的向量空间,有利于网络数据的计算和存储。现有的基于多视图的HIN嵌入方法考虑了节点之间的多种语义关系,但忽略了视图的不完整性。大多数视图存在数据缺失,直接融合多个不完整的视图会导致嵌入效果不佳。为此,文中提出了一种融合不完整多视图的HIN嵌入方法(Incomplete Multi-view Fusion Based HIN Embedding,IMHE)。IMHE的关键思想是聚合其他视图的邻居以重建不完整的视图。由于不同的单视图描述的是同一个网络,因此其他视图中的邻居可以一定程度上恢复不完整视图的结构信息。IMHE首先在不同视图中生成节点序列,并利用多头注意力方法学习单视图嵌入。对于每个不完整视图,IMHE在其他视图中找到缺失节点的k阶邻居,然后将不完整视图中邻居的单视图嵌入聚合在一起,为缺失节点生成新的嵌入。最后使用多视图典型相关性分析方法获得节点的统一嵌入,同时提取多个视图的隐藏语义关系。在3个真实数据集上的实验结果表明,相比现有研究,该方法的嵌入性能有显著提升。
中图分类号:
[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. |
[1] | 吕晓锋, 赵书良, 高恒达, 武永亮, 张宝奇. 基于异质信息网的短文本特征扩充方法 Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network 计算机科学, 2022, 49(9): 92-100. https://doi.org/10.11896/jsjkx.210700241 |
[2] | 杜航原, 李铎, 王文剑. 一种面向电商网络的异常用户检测方法 Method for Abnormal Users Detection Oriented to E-commerce Network 计算机科学, 2022, 49(7): 170-178. https://doi.org/10.11896/jsjkx.210600092 |
[3] | 陈世聪, 袁得嵛, 黄淑华, 杨明. 基于结构深度网络嵌入模型的节点标签分类算法 Node Label Classification Algorithm Based on Structural Depth Network Embedding Model 计算机科学, 2022, 49(3): 105-112. https://doi.org/10.11896/jsjkx.201000177 |
[4] | 郭磊, 马廷淮. 基于好友亲密度的用户匹配 Friend Closeness Based User Matching 计算机科学, 2022, 49(3): 113-120. https://doi.org/10.11896/jsjkx.210200137 |
[5] | 杨旭华, 王磊, 叶蕾, 张端, 周艳波, 龙海霞. 基于节点相似性和网络嵌入的复杂网络社区发现算法 Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding 计算机科学, 2022, 49(3): 121-128. https://doi.org/10.11896/jsjkx.210200009 |
[6] | 蒋宗礼, 樊珂, 张津丽. 基于生成对抗网络和元路径的异质网络表示学习 Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning 计算机科学, 2022, 49(1): 133-139. https://doi.org/10.11896/jsjkx.201000179 |
[7] | 赵金龙, 赵中英. 基于异质信息网络表示学习与注意力神经网络的推荐算法 Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network 计算机科学, 2021, 48(8): 72-79. https://doi.org/10.11896/jsjkx.200800226 |
[8] | 胡昕彤, 沙朝锋, 刘艳君. 基于随机投影和主成分分析的网络嵌入后处理算法 Post-processing Network Embedding Algorithm with Random Projection and Principal Component Analysis 计算机科学, 2021, 48(5): 124-129. https://doi.org/10.11896/jsjkx.200500058 |
[9] | 杨旭华, 王晨. 基于网络嵌入与局部合力的复杂网络社区划分算法 Community Detection Algorithm in Complex Network Based on Network Embedding and Local Resultant Force 计算机科学, 2021, 48(4): 229-236. https://doi.org/10.11896/jsjkx.200200102 |
[10] | 张健雄, 宋坤, 何鹏, 李兵. 基于图神经网络的软件系统中关键类的识别 Identification of Key Classes in Software Systems Based on Graph Neural Networks 计算机科学, 2021, 48(12): 149-158. https://doi.org/10.11896/jsjkx.210100200 |
[11] | 徐新黎, 肖云月, 龙海霞, 杨旭华, 毛剑飞. 基于矩阵分解的属性网络嵌入和社区发现算法 Attributed Network Embedding Based on Matrix Factorization and Community Detection 计算机科学, 2021, 48(12): 204-211. https://doi.org/10.11896/jsjkx.210300060 |
[12] | 高创, 李建华, 季秀怡, 朱程龙, 李诗良, 李洪林. 基于图卷积神经网络的药物靶标作用关系预测方法 Drug Target Interaction Prediction Method Based on Graph Convolutional Neural Network 计算机科学, 2021, 48(10): 127-134. https://doi.org/10.11896/jsjkx.200700068 |
[13] | 丁钰, 魏浩, 潘志松, 刘鑫. 网络表示学习算法综述 Survey of Network Representation Learning 计算机科学, 2020, 47(9): 52-59. https://doi.org/10.11896/jsjkx.190300004 |
[14] | 蒋宗礼, 李苗苗, 张津丽. 基于融合元路径图卷积的异质网络表示学习 Graph Convolution of Fusion Meta-path Based Heterogeneous Network Representation Learning 计算机科学, 2020, 47(7): 231-235. https://doi.org/10.11896/jsjkx.190600085 |
[15] | 吴勇, 王斌君, 翟一鸣, 仝鑫. 共引增强有向网络嵌入研究 Study on Co-citation Enhancing Directed Network Embedding 计算机科学, 2020, 47(12): 279-284. https://doi.org/10.11896/jsjkx.191000199 |
|