Heterogeneous information network (HIN) are becoming popular across multiple applications in forms of complex large-scaled networked data such as social networks, bibliographic networks, biological networks, etc. Recently, information network embedding (INE) has aroused tremendously interests from researchers due to its effectiveness in information network analysis and mining tasks. From recent views of INE, community is considered as the mesoscopic preserving network’s structure which can be combined with traditional approach of network’s node proximities (microscopic structure preserving) to leverage the quality of network’s representation. Most of contemporary INE models, like as: HIN2Vec, Metapath2Vec, HINE, etc. mainly concentrate on microscopic network structure preserving and ignore the mesoscopic (intra-community) structure of HIN. In this paper, we introduce a novel approach of topic-driven meta-path-based embedding, namely W-Com2Vec (Weighted intra-community to vector). Our proposed W-Com2Vec model enables to capture richer semantic of node representation by applying the meta-path-based community-aware, node proximity preserving and topic similarity evaluation at the same time during the process of network embedding. We demonstrate comprehensive empirical studies on our proposed W-Com2Vec model with several real-world HINs. Experimental results show W-Com2Vec outperforms recent state-of-the-art INE models in solving primitive network analysis and mining tasks.
Heterogeneous Information Network Embedding for Mention Recommendation
