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.

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

Ranked #4 on Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

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

Ranked #1 on Link Prediction on Citeseer

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

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

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

Ranked #1 on Graph Classification on RE-M5K

GRAPH CLASSIFICATION GRAPH REGRESSION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

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

Recently, a few studies have focused on learning temporal information in addition to the topology of a graph.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING UNSUPERVISED REPRESENTATION LEARNING

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

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

Ranked #1 on Node Classification on Wikipedia

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

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.

Ranked #1 on Graph Classification on REDDIT-MULTI-12K

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

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