Relation Structure-Aware Heterogeneous Information Network Embedding
Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringLink PredictionNetwork EmbeddingNode ClassificationNode ClusteringRelationSimilar Papers 제목 키워드 기반
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for netw…
ClusteringNode ClassificationSHGNN: Structure-Aware Heterogeneous Graph Neural Network
Many real-world graphs (networks) are heterogeneous with different types of nodes and edges. Heterogeneous graph embedding, aiming at learning the low-dimensional node representations of a heterogeneous graph, is vital f…
Graph EmbeddingGraph Neural NetworkNode ClassificationRHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale h…
Contrastive LearningGraph Neural NetworkGraph Representation LearningRelation+1Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG …
Entity AlignmentEntity EmbeddingsKnowledge GraphsRelationRelation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction
Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning r…
AttributeContrastive LearningRelation