About

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.

Source: SIGN: Scalable Inception Graph Neural Networks

Benchmarks

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Subtasks

Datasets

Greatest papers with code

Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/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.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

15 Sep 2020dmlc/dgl

Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

2 Feb 2020shaoxiongji/awesome-knowledge-graph

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

4 KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING RELATIONAL REASONING

How Powerful are Graph Neural Networks?

ICLR 2019 weihua916/powerful-gnns

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

GRAPH CLASSIFICATION GRAPH REGRESSION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

CogDL: An Extensive Toolkit for Deep Learning on Graphs

1 Mar 2021THUDM/cogdl

Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION RECOMMENDATION SYSTEMS

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

26 Feb 2019benedekrozemberczki/pytorch_geometric_temporal

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.

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

22 Nov 2017hwwang55/GraphGAN

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

Hierarchical Graph Representation Learning with Differentiable Pooling

NeurIPS 2018 RexYing/graph-pooling

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.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

Hyperbolic Neural Networks

NeurIPS 2018 HazyResearch/hgcn

However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.

GRAPH REPRESENTATION LEARNING NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDINGS