116 papers with code • 1 benchmarks • 4 datasets

**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

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.

Ranked #1 on KG-to-Text Generation on WikiGraphs

In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.

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.

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).

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

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.

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

It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, graph classification, etc.

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