Improved Deep Embeddings for Inferencing with Multi-Layered Graphs

Abstract

Inferencing with graph data necessitates the mapping of its nodes into a vector space, where the relationships are preserved. However, with multi-layered graphs, where multiple types of relationships exist for the same set of nodes, it is crucial to exploit the information shared between layers, in addition to the distinct aspects of each layer. In this paper, we propose a novel approach that first obtains node embeddings in all layers jointly via DeepWalk on a supra graph, which allows interactions between layers, and then fine-tunes the embeddings to encourage cohesive structure in the latent space. With empirical studies in node classification, link prediction and multi-layered community detection, we show that the proposed approach outperforms existing single-and multi-layered graph embedding algorithms on several benchmarks. In addition to effectively scaling to a large number of layers (tested up to 37), our approach consistently produces highly modular community structure, even when compared to methods that directly optimize for the modularity function.