34 papers with code ·
Graphs

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

SOTA for Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

ICLR 2019 • benedekrozemberczki/CapsGNN •

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.

SOTA for Graph Classification on ENZYMES

benedekrozemberczki/graph2vec •

•Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

SOTA for Graph Classification on NCI109

ICLR 2019 • weihua916/powerful-gnns •

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

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 NEURAL NETWORK GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

CVPR 2017 • mys007/ecc •

A number of problems can be formulated as prediction on graph-structured data.

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

KDD 2018 • benedekrozemberczki/GAM •

Graph classification is a problem with practical applications in many different domains.

SOTA for Graph Classification on HIV dataset

Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice.

giannisnik/cnn-graph-classification •

•Graph kernels have been successfully applied to many graph classification problems.