R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need meta-path, which is much improved compared to the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves the state-of-the-art performance on the ogbn-mag large scale heterogeneous graph dataset.
Code (1)
Tasks
Graph Neural NetworkRelationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
Many real-world heterogeneous graphs exhibit pronounced heterophily, where connected nodes often have dissimilar labels or play different semantic roles. In such settings, standard heterogeneous graph neural networks tha…
Representation LearningGraph LearningHeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However, previous GSL methods assume that graph…
Graph LearningGraph MatchingGraph SimilarityGraph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally rep…
Graph LearningGraph Neural NetworkCompressed Heterogeneous Graph for Abstractive Multi-Document Summarization
Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to repres…
DecoderDiversityDocument SummarizationGraph Similarity+1A Weighted Heterogeneous Graph Based Dialogue System
Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and…
AttributeDialogue ManagementManagement