期刊
WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018)
卷 -, 期 -, 页码 499-508出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3178876.3186116
关键词
Graph convolutional networks; Semi-supervised learning; Graph diffusion; Adjacency matrix; Pointwise mutual information
资金
- JSPS KAKENHI [16K12532, 15J01402]
- MIC SCOPE [172307001]
- Grants-in-Aid for Scientific Research [16K12532, 15J01402] Funding Source: KAKEN
The problem of extracting meaningful data through graph analysis spans a range of different fields, such as the internet, social networks, biological networks, and many others. The importance of being able to effectively mine and learn from such data continues to grow as more and more structured data become available. In this paper, we present a simple and scalable semi-supervised learning method for graph-structured data in which only a very small portion of the training data are labeled. To sufficiently embed the graph knowledge, our method performs graph convolution from different views of the raw data. In particular, a dual graph convolutional neural network method is devised to jointly consider the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Given the different data transformations from the two networks, we then introduce an unsupervised temporal loss function for the ensemble. In experiments using both unsupervised and supervised loss functions, our method outperforms state-of-the-art techniques on different datasets.
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