Structure-Enhanced Heterogeneous Graph Contrastive Learning | |
Zhu, Yanqiao1,4; Xu, Yichen2; Cui, Hejie3; Yang, Carl3; Liu, Qiang1,4; Wu, Shu1,4 | |
2022-03 | |
会议日期 | 2022-3 |
会议地点 | Online |
页码 | 82-90 |
英文摘要 | Recent years have seen a growing interest in Graph Contrastive Learning (GCL), which trains Graph Neural Network (GNN) model to discriminate similar and dissimilar pairs of nodes without human annotations. Most prior GCL work focuses on homogeneous graphs and little attention has been paid to Heterogeneous Graphs (HGs) that involve different types of nodes and edges. Moreover, earlier studies reveal that the explicit use of structure information of un- derlying graphs is useful for learning representations. Conventional GCL methods merely measure the likelihood of contrastive pairs according to node representations, which may not align with the true semantic similarities. How to leverage such structure information for GCL is not yet well-understood. To address the aforementioned challenges, this paper presents a novel method dubbed STructure- EnhaNced heterogeneous graph ContrastIve Learning, STENCIL for brevity. At first, we generate multiple semantic views for HGs based on metapaths. Unlike most methods that maximize the consis- tency among these views, we propose a novel multiview contrastive aggregation objective to adaptively distill information from each view. In addition, we advocate the explicit use of structure embed- ding, which enriches the model with local structural patterns of the underlying HGs, so as to better mine true and hard negatives for GCL. Empirical studies on three real-world datasets show that our proposed method consistently outperforms existing state-of-the-art methods and even surpasses several supervised counterparts. |
源文献作者 | SIAM |
会议录出版者 | SIAM |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48469] |
专题 | 自动化研究所_智能感知与计算研究中心 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 2.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China 3.Department of Computer Science, Emory University, GA, USA 4.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Zhu, Yanqiao,Xu, Yichen,Cui, Hejie,et al. Structure-Enhanced Heterogeneous Graph Contrastive Learning[C]. 见:. Online. 2022-3. |
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