
DGL
 Referenced in 11 articles
[sw33907]
 distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable...

GNNExplainer
 Referenced in 3 articles
[sw37864]
 Graph Neural Networks. Graph Neural Networks (GNNs) are a powerful tool for machine learning ... complex models, and explaining predictions made by GNNs remains unsolved. Here we propose GNNExplainer ... giving insights into errors of faulty GNNs...

EigenGNN
 Referenced in 2 articles
[sw38084]
 Graph Structure Preserving Plugin for GNNs. Graph Neural Networks (GNNs) are emerging machine learning ... models on graphs. Although sufficiently deep GNNs are shown theoretically capable of fully preserving graph ... empirically and analytically that the existing shallow GNNs cannot preserve graph structures well. To overcome ... general plugin module to boost GNNs ability in preserving graph structures. Specifically, we integrate...

PyG
 Referenced in 4 articles
[sw41050]
 easily write and train Graph Neural Networks (GNNs) for a wide range of applications related...

PairNorm
 Referenced in 2 articles
[sw38087]
 PairNorm: Tackling Oversmoothing in GNNs. The performance of graph neural nets (GNNs) is known ... problem setting that benefits from deeper GNNs. Code is available at https://github.com/LingxiaoShawn/PairNorm...

XGNN
 Referenced in 1 article
[sw37866]
 Graph Neural Networks. Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor ... promising performance on many graph tasks. However, GNNs are mostly treated as blackboxes ... novel approach, known as XGNN, to interpret GNNs at the modellevel. Our approach ... level insights and generic understanding of how GNNs work. In particular, we propose to explain...

FedGraphNN
 Referenced in 1 article
[sw41837]
 rapidly growing thanks to the capacity of GNNs in learning distributed representations from graphstructured ... suitable platform for the FL of GNNs. To this end, we introduce FedGraphNN, an open ... system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation ... exposing significant challenges in graph FL: federated GNNs perform worse in most datasets with...

MIMOSA
 Referenced in 1 article
[sw41883]
 pretrains two property agnostic graph neural networks (GNNs) for molecule topology and substructuretype prediction ... ring. For each iteration, MIMOSA uses the GNNs’ prediction and employs three basic substructure operations...

AliGraph
 Referenced in 1 article
[sw38083]
 efficiently support not only existing popular GNNs but also a series of inhouse developed...

DGLLifeSci
 Referenced in 1 article
[sw39641]
 Graphs in Life Science. Graph neural networks (GNNs) constitute a class of deep learning methods...

GraphSAGE
 Referenced in 4 articles
[sw33908]
 GraphSAGE is a framework for inductive representation learning...

DropEdge
 Referenced in 3 articles
[sw37753]
 DropEdge: Towards Deep Graph Convolutional Networks on Node...