Heterogeneous Node Classification
14 papers with code • 7 benchmarks • 8 datasets
Node classification in heterogeneous graphs, where nodes and/or edges have multiple types.
Benchmarks
These leaderboards are used to track progress in Heterogeneous Node Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Heterogeneous Node Classification models and implementationsDatasets
DBLP
SupplyGraph
Most implemented papers
Graph Attention Networks
PetarV-/GAT
•
•
ICLR 2018
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
•
•
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Heterogeneous Graph Transformer
acbull/pyHGT
•
•
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Simple 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.
Graph Transformer Networks
seongjunyun/Graph_Transformer_Networks
•
•
NeurIPS 2019
In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.
Heterogeneous Deep Graph Infomax
YuxiangRen/Heterogeneous-Deep-Graph-Infomax
•
•
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Non-local Attention Learning on Large Heterogeneous Information Networks
xiaoyuxin1002/NLAH
•
•
2019 IEEE International Conference on Big Data (Big Data) 2019
In this way, it leverages both local and non-local information simultaneously.
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
didi/hetsann
•
•
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.
Scalable Graph Neural Networks for Heterogeneous Graphs
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data.
