W-Metagraph2Vec: a novel approval of enriched schematic topic-driven heterogeneous information network embedding

Abstract

Recently, heterogeneous information network (HIN) embedding is wide studied due to its various applications. In general, network embedding is a way of representation network’s nodes into a low-dimensional space. Most of previous embedding techniques concentrate on the homogeneous networks only in which all nodes are considered as a single type. Heterogeneous network embedding is a challenging problem due to the complexity of different node’s types and link’s types. Recent heterogeneous network embedding studies are based on meta-path and meta-graph to guide the random walks over the networks. These heterogeneous network embedding approaches outperform state-of-the-art homogeneous embedding models in multiple heterogeneous network mining tasks. However, recent meta-graph-based approaches are ineffective in capturing topic similarity between nodes. There is no doubt that most of common HINs (DBLP, Facebook, etc.) are rich-text which contain many text-based nodes, such as paper, comment, post, etc. In this paper, we propose a novel embedding approach, namely W-MetaGraph2Vec. The W-MetaGraph2Vec uses the topic-driven meta-graph-based random walk mechanism in weighted HIN to guide the generation of heterogeneous neighborhood of a node. Extensive experiments on real-world datasets demonstrate that our proposed model not only leverage HIN mining tasks, such as node similarity search, clustering, classification, etc. in performance accuracy but also discern the problems of topic relevance between text-based nodes.