MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction
| Autor: | Zhanlin Liu, Jiaming Huang, Yucong Duan, Zhiqiang Zhang, Geyu Tang, Yifan Yang, Zhao Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Matrix completion
General Computer Science Computer science Graph embedding General Engineering Recommender system Multi-view computer.software_genre Convolutional neural network Graph graph convolutional network Graph (abstract data type) General Materials Science Data mining lcsh:Electrical engineering. Electronics. Nuclear engineering computer matrix completion lcsh:TK1-9971 Heterogeneous network link prediction |
| Zdroj: | IEEE Access, Vol 7, Pp 176317-176328 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods. |
| Databáze: | OpenAIRE |
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