Graph Homomorphism Convolution

Jul 12, 2020



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 proved that graph homomorphism numbers provide a natural universally invariant embedding maps which can be used for graph classifications. We also discovered that the graph homomorphism method unifies connectivity preserving methods. In practice, by observing that graph classification datasets often have bounded treewidths, we show that our method is not only competitive in classification accuracy but also run much faster than other state-of-the-art. Finally, based on our theoretical analysis, we propose the Graph Homomorphism Convolution module which has promising performance in the graph classification task.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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