In this talk, we introduce a various methods of representations of graphs which are mathematical objects expressing a variety of non-Euclidean data such as Molecules, social networks, genes, transportation networks, citation networks of papers and so on. Graph representation as a Euclidean vector is inevitable in machine learning for classifications for graphs which is closely related to graph neural network in computer science. We would like to introduce a few literatures, Weisfeiler-lehman algortihm, random walks, graph convolution whci are commonly used techniques and explain the result of combining them with topological invarints of graphs
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