Presentation information

International Session

International Session » ES-2 Machine learning

[4S1-IS-2f] Machine learning

Fri. Jun 17, 2022 10:00 AM - 11:20 AM Room S (Online S)

Chair: Hisashi Kashima (Kyoto University)

11:00 AM - 11:20 AM

[4S1-IS-2f-04] Common Subgraph Extraction based on Link Prediction

〇Jianming Huang1, Hiroyuki Kasai1 (1. WASEDA University)


Keywords:Machine Learning, Graph Classification, Generative Model

Many state-of-the-art methods for graph classification are based on the graph convolution framework and the message-passing mechanism, which tends to use a convolution-like operation to aggregate the features of vertex neighbors and pass the information to nearby vertices. However, recent researches reveal that there usually exists a heavy batch noise of graphs because of the diverse graph structures, which means that not all parts of a graph is useful for classification, most of them contain a huge amount of noise, which will be also aggregated into vertex features when doing graph convolutions and graph poolings. To overcome these difficulties, we propose a generative graph model that focuses on the link prediction problem of a given set of vertex. Through a loop of predicting link, we can construct one or several common subgraphs from the given graph set, which helps the graph classification.

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