Browse SoTA > Graphs > Representation Learning > Graph Representation Learning

Graph Representation Learning

50 papers with code · Graphs

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

No evaluation results yet. Help compare methods by submit evaluation metrics.

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

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

2 Feb 2020shaoxiongji/awesome-knowledge-graph

In this survey, we provide a comprehensive review on 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.

GRAPH REPRESENTATION LEARNING 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 REPRESENTATION LEARNING

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

Heterogeneous Graph Attention Network

WWW 2019 2019 Jhy1993/HAN

With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.

GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION

Hierarchical Graph Representation Learning with Differentiable Pooling

NeurIPS 2018 RexYing/diffpool

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

Multi-Task Graph Autoencoders

7 Nov 2018vuptran/graph-representation-learning

We examine two fundamental tasks associated with graph representation learning: link prediction and node classification.

 Ranked #1 on Link Prediction on Citeseer (Accuracy metric)

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

Learning to Make Predictions on Graphs with Autoencoders

23 Feb 2018vuptran/graph-representation-learning

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification.

GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING 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