BHGNN-RT: Network embedding for directed heterogeneous graphs
Networks are one of the most valuable data structures for modeling problems in the real world. However, the most recent node embedding strategies have focused on undirected graphs, with limited attention to directed graphs, especially directed heterogeneous graphs. In this study, we first investigated the network properties of directed heterogeneous graphs. Based on network analysis, we proposed an embedding method, a bidirectional heterogeneous graph neural network with random teleport (BHGNN-RT), for directed heterogeneous graphs, that leverages bidirectional message-passing process and network heterogeneity. With the optimization of teleport proportion, BHGNN-RT is beneficial to overcome the over-smoothing problem. Extensive experiments on various datasets were conducted to verify the efficacy and efficiency of BHGNN-RT. Furthermore, we investigated the effects of message components, model layer, and teleport proportion on model performance. The performance comparison with all other baselines illustrates that BHGNN-RT achieves state-of-the-art performance, outperforming the benchmark methods in both node classification and unsupervised clustering tasks.
Code (1)
Tasks
Graph Neural NetworkNetwork EmbeddingNode ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Embedding Directed Graphs in Potential Fields Using FastMap-D
Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, E…
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed w…
Graph EmbeddingLow-dimensional statistical manifold embedding of directed graphs
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a pro…
Node Representation Learning for Directed Graphs
We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue th…
General ClassificationGraph ReconstructionLink PredictionMulti-Label Classification+2A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphs
Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information abou…
Graph Embedding