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https://github.com/benedekrozemberczki/graph2vec
Graph2Vec is an embedding algorithm which learns representations for a set of graphs using implicit factorization. The procedure places graphs in an abstract feature space where graphs with similar structural properties (Weisfehler-Lehman features) are clustered together. Graph2Vec has a linear runtime complexity in the number of graphs in the dataset which makes it extremely scalable. This specific implementation supports multi-core data processing in the feature extraction and factorization phases. (So far this is the only implementation which support multi-core processing in every phase).
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