Paper ID: 2101.01425 • Published Jan 5, 2021
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding
Mauricio Soto-Gomez, Peter Robinson, Carlos Cano, Ali Pashaeibarough, Emanuele Cavalleri, Justin Reese, Marco Mesiti, Giorgio...
TL;DR
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Many real-world problems are naturally modeled as heterogeneous graphs, where
nodes and edges represent multiple types of entities and relations. Existing
learning models for heterogeneous graph representation usually depend on the
computation of specific and user-defined heterogeneous paths, or in the
application of large and often not scalable deep neural network architectures.
We propose Het-node2vec, an extension of the node2vec algorithm, designed for
embedding heterogeneous graphs. Het-node2vec addresses the challenge of
capturing the topological and structural characteristics of graphs and the
semantic information underlying the different types of nodes and edges of
heterogeneous graphs, by introducing a simple stochastic node and edge type
switching strategy in second order random walk processes. The proposed approach
also introduces an ''attention mechanism'' to focus the random walks on
specific node and edge types, thus allowing more accurate embeddings and more
focused predictions on specific node and edge types of interest. Empirical
results on benchmark datasets show that Hetnode2vec achieves comparable or
superior performance with respect to state-of-the-art methods for heterogeneous
graphs in node label and edge prediction tasks.