69 papers with code ·
Graphs

( Image credit: Hierarchical Graph Pooling with Structure Learning )

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

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.

SOTA for Node Classification on Cora (using extra training data)

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 SKELETON BASED ACTION RECOGNITION

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

SOTA for Node Classification on AIFB

GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION

Therefore, how to ﬁnd a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

#2 best model for Graph Classification on BP-fMRI-97

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

#2 best model for Graph Classification on IPC-grounded

DRUG DISCOVERY GRAPH CLASSIFICATION NODE CLASSIFICATION SQL-TO-TEXT

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

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

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

Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs.