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 representat

conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. „e metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model

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