Fast Sequence Based Embedding with Diffusion Graphs

CompleNet 2018  ·  Benedek Rozemberczki, Rik Sarkar ·

A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes. Vertex sequence based embedding procedures use features extracted from linear sequences of vertices to create embeddings using a neural network. In this paper, we propose diffusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational efficiency is superior to previous methods due to simpler sequence generation, and it produces more ac-curate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph.In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence based embedding methods.



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