Heterogeneous Node Classification
7개 벤치마크 · 논문 17편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Graph Attention Networks
Semi-Supervised Classification with Graph Convolutional Networks
Modeling Relational Data with Graph Convolutional Networks
Heterogeneous Graph Transformer
Simple and Efficient Heterogeneous Graph Neural Network
Papers
HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for…
Heterogeneous Node ClassificationGraph LearningFrom Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets becom…
Graph RegressionHeterogeneous Node ClassificationNode ClassificationRelation PredictionSlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing iss…
Graph Neural NetworkHeterogeneous Node ClassificationLink PredictionNode ClassificationEfficient Heterogeneous Graph Learning via Random Projection
Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world g…
Graph LearningGraph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionSimple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph…
Graph Neural NetworkHeterogeneous Node ClassificationNode Property PredictionAre we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements. In this work, …
BenchmarkingHeterogeneous Node Classification