Heterogeneous Graph Attention Network
| Autor: | Yanfang Ye, Houye Ji, Chuan Shi, Bai Wang, Xiao Wang, Peng Cui, Philip S. Yu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Power graph analysis Theoretical computer science Artificial neural network Social network Computer science business.industry Deep learning Computer Science - Social and Information Networks 02 engineering and technology Graph 020204 information systems Attention network 0202 electrical engineering electronic engineering information engineering Embedding Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business Interpretability |
| Zdroj: | WWW |
| Popis: | Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis. 10 pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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