Home >

news ヘルプ

論文・著書情報


タイトル
和文: 
英文:Graph Homomorphism Convolution 
著者
和文: Hoang Nguyen, 前原 貴憲.  
英文: Hoang Nguyen, Takanori Maehara.  
言語 English 
掲載誌/書名
和文: 
英文:Proceedings of Machine Learning Research: 
巻, 号, ページ Vol. 119       
出版年月 2020年6月13日 
出版者
和文: 
英文:PMLR 
会議名称
和文: 
英文:37th International Conference on Machine Learning 
開催地
和文: 
英文: 
公式リンク http://proceedings.mlr.press/v119/nguyen20c.html
 
アブストラクト In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from F to G, where G is a graph of interest (e.g. molecules or social networks) and F belongs to some family of graphs (e.g. paths or non-isomorphic trees). We show that graph homomorphism numbers provide a natural invariant (isomorphism invariant and F-invariant) embedding maps which can be used for graph classification. Viewing the expressive power of a graph classifier by the F-indistinguishable concept, we prove the universality property of graph homomorphism vectors in approximating F-invariant functions. In practice, by choosing F whose elements have bounded tree-width, we show that the homomorphism method is efficient compared with other methods.

©2007 Institute of Science Tokyo All rights reserved.