# MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

## TL;DR

MetaGraph2Vec introduces a novel metagraph-based approach for embedding in heterogeneous information networks, capturing richer semantic contexts and outperforming existing methods in various network analysis tasks.

## Contribution

The paper proposes a new metagraph concept and an embedding algorithm, MetaGraph2Vec, to better capture complex relationships in heterogeneous networks.

## Key findings

- MetaGraph2Vec outperforms state-of-the-art baselines in node classification.
- The method improves clustering and similarity search in heterogeneous networks.
- Richer structural contexts enhance embedding quality in sparse HINs.

## Abstract

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.