metapath2vec: Scalable Representation Learning for ...

Metapath2vec: Scalable Representation Learning forHeterogeneous NetworksYuxiao Dong Microso ResearchRedmond, WA 98052yuxdong@microso .comNitesh V. ChawlaUniversity of Notre DameNotre Dame, IN SwamiArmy Research LaboratoryAdelphi, MD study the problem of Representation Learning in heterogeneousnetworks. Its unique challenges come from the existence of mul-tiple types of nodes and links, which limit the feasibility of theconventional network embedding techniques. We develop twoscalable Representation Learning models, namelymetapath2vecandmetapath2vec++. emetapath2vecmodel formalizes meta-path-based random walks to construct the heterogeneous neighborhoodof a node and then leverages a heterogeneous skip-gram modelto perform node embeddings. emetapath2vec++model furtherenables the simultaneous modeling of structural and semantic cor-relations in heterogeneous networks. Extensive experiments showthatmetapath2vecandmetapath2vec++are able to not only outper-form state-of-the-art embedding models in various heterogeneousnetwork mining tasks, such as node classi cation, clustering, andsimilarity search, but also discern the structural and semantic cor-relations between diverse network CONCEPTS Information systems Social networks; Computing method-ologies Unsupervised Learning ; Learning latent represen-tations;Knowledge Representation and reasoning;KEYWORDSN etwork Embedding; Heterogeneous Representation Learning ; La-tent Represent

representation learning methods enable the automatic discovery of useful and meaningful (latent) features from the “raw networks.” However, these work has thus far focused on representation learning for homogeneous networks—representative of singular type of nodes and relationships. Yet a large number of social and

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