53 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.

#2 best model for Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

CVPR 2018 • rusty1s/pytorch_geometric •

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.

SOTA for Graph Classification on IPC-lifted

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

Microsoft/gated-graph-neural-network-samples •

•Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

SOTA for Graph Classification on IPC-lifted

DRUG DISCOVERY GRAPH CLASSIFICATION NODE CLASSIFICATION SQL-TO-TEXT

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.

#2 best model for Graph Classification on RE-M5K

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.

#9 best model 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.

SOTA for Graph Classification on REDDIT-B

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

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.