  Network embedding in heterogeneous information networks (HINs) is a
challenging task, due to complications of different node types and rich
relationships between nodes. As a result, conventional network embedding
techniques cannot work on such HINs. Recently, metapath-based approaches have
been proposed to characterize relationships in HINs, but they are ineffective
in capturing rich contexts and semantics between nodes for embedding learning,
mainly because (1) metapath is a rather strict single path node-node
relationship descriptor, which is unable to accommodate variance in
relationships, and (2) only a small portion of paths can match the metapath,
resulting in sparse context information for embedding learning. In this paper,
we advocate a new metagraph concept to capture richer structural contexts and
semantics between distant nodes. A metagraph contains multiple paths between
nodes, each describing one type of relationships, so the augmentation of
multiple metapaths provides an effective way to capture rich contexts and
semantic relations between nodes. This greatly boosts the ability of
metapath-based embedding techniques in handling very sparse HINs. We propose a
new embedding learning algorithm, namely MetaGraph2Vec, which uses metagraph to
guide the generation of random walks and to learn latent embeddings of
multi-typed HIN nodes. Experimental results show that MetaGraph2Vec is able to
outperform the state-of-the-art baselines in various heterogeneous network
mining tasks such as node classification, node clustering, and similarity
search.
