|
|
|
| Heterogeneous Hypernetwork Representation Learning with the Translation Constraint |
| LIU Zhenguo1, ZHU Yu1, ZHAO Haixing2, WANG Xiaoying1, HUANG Jianqiang1 |
1.Department of Computer Technology and Application, Qinghai University, Xi'ning, Qinghai 810000, China;
2.State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xi'ning, Qinghai 810000, China |
|
|
|
|
Abstract In contrast to the ordinary network with only pairwise relationships between the nodes, there also exist complex tuple relationships (i.e. the hyperedges) among the nodes in the hypernetwork. However, most of the existing network representation learning methods cannot effectively capture complex tuple relationships. Therefore, to resolve this issue, a heterogeneous hypernetwork representation learning method with the translation constraint (HRTC) is proposed. Firstly, the proposed method combines clique expansion and star expansion to transform a heterogeneous hypernetwork abstracted as the hypergraph into a heterogeneous network abstracted as 2-section graph+incidence graph. Secondly, a meta-path walk method aware of semantic relevance of the nodes (SRwalk) is proposed to capture semantic relationships between the nodes. Finally, while the pairwise relationships between the nodes are trained, the tuple relationships among the nodes are captured by introducing the translation mechanism in knowledge representation learning. Experimental results show that as for the link prediction task, the performance of the proposed method is close to that of other optimal baseline methods, and as for the hypernetwork reconstruction task, the performance of the proposed method is better than that of other optimal baseline methods on the drug dataset for case beyond 0.6 hyperedge reconstruction ratio, meanwhile, the average performance of the proposed method outperforms that of other optimal baseline methods by 16.24% on the GPS dataset.
|
|
Received: 26 June 2022
|
|
|
|
|
|
[1] 王岩,唐杰. 网络表示学习算法的分析与验证[J]. 中文信息学报,2019,33 (2): 97-104.
[2] 吴越,王英,王鑫,等. 基于超图卷积的异质网络半监督节点分类[J]. 计算机学报,2021,44(11): 2248-2260.
[3] 张宁豫,陈曦,陈矫彦,等. 基于位置的知识图谱链接预测[J]. 中文信息学报,2018,32(4): 80-86,129.
[4] 蒋盛益,杨博泓,姚娟娜,等. 一种基于增广网络的快速微博社区检测算法[J]. 中文信息学报,2016,30(5): 65-72.
[5] 胡秉德,王新根,王新宇,等. 超图学习综述:算法分类与应用分析[J]. 软件学报,2022,33(2): 498-523.
[6] Agarwal S,Branson K,Belongie S. Higher order learning with graphs[C]//Proceedings of the 23rd International Conference on Machine Learning,2006: 17-24.
[7] Huang J,Chen C,Ye F H,et al. Hyper2Vec: Biased random walk for hyper-network embedding[C]//Proceedings of the International Conference on Database Systems for Advanced Applications,2019: 273-277.
[8] Huang J,Liu X,Song Y Q. Hyper-path-based representation learning for hyper-networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management,2019: 449-458.
[9] Tu K,Cui P,Wang X,et al. Structural deep embedding for hypernetworks[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence,2018: 426-433.
[10] Mikolov T,Sutskever I,Chen Kai,et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of International Conference on Neural Information Processing Systems,2013: 3111-3119.
[11] 郑玉艳,王明省,石川,等. 异质信息网络中基于元路径的社团发现算法研究[J]. 中文信息学报,2018,32(9): 132-142.
[12] Bretto A. Hypergraph theory: An introduction[M]. Cham: Springer,2013.
[13] Bordes A,Usunier N,Garciaduran A. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems,2013: 2787-2795.
[14] Zheng V W,Cao B,Zheng Y,et al. Collaborative filtering meets mobile recommendation: A user-centered approach[C]//Proceedings of the 24th AAAI Conference on Artificial Intelligence,2010: 236-241.
[15] Harper F M,Konstan J A. The movielens datasets: History and context[J]. Ksii Transactions on Internet and Information Systems,2016,5(4):19.
[16] 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,2014: 701-710.
[17] Grover A,Leskovec J.Node2Vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016: 855-864.
[18] 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.
[19] Fu G,Yuan B,Duan Q,et al. Representation learning for heterogeneous information networks via embedding events[C]//Proceedings of the International Conference on Neural Information Processing,2019: 327-339.
[20] 姜正申,刘宏志,付彬,等. 集成学习的泛化误差和AUC分解理论及其在权重优化中的应用[J].计算机学报,2019,42(1): 1-15. |
|
|
|
2022, Vol. 36 
