Graph Representation Learning
471 papers with code • 1 benchmarks • 6 datasets
The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.
Benchmarks
These leaderboards are used to track progress in Graph Representation Learning
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Libraries
Use these libraries to find Graph Representation Learning models and implementations
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IMDB-BINARY
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Most implemented papers
How Powerful are Graph Neural Networks?
weihua916/powerful-gnns
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ICLR 2019
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Hierarchical Graph Representation Learning with Differentiable Pooling
dmlc/dgl
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NeurIPS 2018
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
IBM/EvolveGCN
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Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT/GraphSAINT
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ICLR 2020
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
Fast Graph Representation Learning with PyTorch Geometric
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
michiyasunaga/qagnn
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NAACL 2021
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
A Fair Comparison of Graph Neural Networks for Graph Classification
diningphil/gnn-comparison
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ICLR 2020
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
SIGN: Scalable Inception Graph Neural Networks
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.
Do Transformers Really Perform Bad for Graph Representation?
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
