Biasing second-order random walk sampling for heterogeneous graph embedding

We present heterogeneous-node2vec, a novel method that leverages the well-known node2vec algorithm to enable the generation of random-walk samples in a heterogeneous context. Specifically, we propose a strategy to bias the random walk, enabling type-aware transitions between different node and edge types. We evaluate the proposed technique on node-label prediction tasks, applied to various real-world, complex networks. A comparison with state-of-the-art techniques for heterogeneous graph embedding demonstrates that our strategy achieves competitive results for node-label prediction. This evidences that graph representation methods based on heterogeneous random-walk sampling can attain strong performance on standard supervised tasks when the sampling procedure incorporates the semantic information defined by the type heterogeneity of entities within the graph. This approach provides an effective and scalable solution for representing and learning from complex heterogeneous graphs.