110 papers with code ·
Graphs

( Image credit: Hierarchical Graph Pooling with Structure Learning )

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

Ranked #2 on Graph Classification on D&D

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 #2 on 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.

Ranked #1 on Node Classification on Cora

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION

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.

Ranked #1 on Graph Classification on IPC-lifted

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

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.

Ranked #2 on Graph Classification on BP-fMRI-97

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

Ranked #1 on Node Classification on AIFB

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

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

Ranked #2 on 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.

Ranked #3 on Graph Classification on RE-M5K

In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales.