
TensorFlow
 Referenced in 653 articles
[sw15170]
 graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated ... conducting machine learning and deep neural networks research, but the system is general enough...

DGL
 Referenced in 11 articles
[sw33907]
 GraphCentric, HighlyPerformant Package for Graph Neural Networks. Advancing research in the emerging field...

GNNExplainer
 Referenced in 3 articles
[sw37864]
 GNNExplainer: Generating Explanations for Graph Neural Networks. Graph Neural Networks (GNNs) are a powerful tool ... feature information with the graph structure by recursively passing neural messages along edges...

Devign
 Referenced in 3 articles
[sw40145]
 Learning Comprehensive Program Semantics via Graph Neural Networks. Vulnerability identification is crucial to protect ... graphs and the recent advance on graph neural networks, we propose Devign, a general graph ... neural network based model for graphlevel classification through learning on a rich...

TUDataset
 Referenced in 3 articles
[sw37862]
 learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark ... address this, we introduce the TUDataset for graph classification and regression. The collection consists ... Pythonbased data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here...

PyG
 Referenced in 4 articles
[sw41050]
 PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range ... various methods for deep learning on graphs and other irregular structures, also known as geometric...

CayleyNets
 Referenced in 5 articles
[sw38090]
 CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. The rise of graphstructured...

CogDL
 Referenced in 2 articles
[sw37740]
 several realworld applications such as social network analysis and largescale recommender systems ... graph domain, including node classification, link prediction, graph classification, and other graph tasks. For each ... major parts, graph embedding methods and graph neural networks. Most of the graph embedding methods ... properties such as structural information, while graph neural networks capture node features and work...

SuperGlue
 Referenced in 2 articles
[sw42666]
 SuperGlue: Learning Feature Matching with Graph Neural Networks. This paper introduces SuperGlue, a neural network ... whose costs are predicted by a graph neural network. We introduce a flexible context aggregation...

Pixel2Mesh
 Referenced in 5 articles
[sw31205]
 Limited by the nature of deep neural network, previous methods usually represent a 3D shape ... represents 3D mesh in a graphbased convolutional neural network and produces correct geometry...

DeepTMA
 Referenced in 2 articles
[sw33597]
 Contention Models for Network Calculus using Graph Neural Networks. Network calculus computes ... delay bounds for individual data flows in networks of aggregate schedulers. It searches ... between these flows at each scheduler. Analyzing networks, this leads to complex dependency structures ... contention model in one location of the network can have huge impact...

XGNN
 Referenced in 1 article
[sw37866]
 XGNN: Towards ModelLevel Explanations of Graph Neural Networks. Graphs neural networks (GNNs) learn node ... which have achieved promising performance on many graph tasks. However, GNNs are mostly treated...

FedGraphNN
 Referenced in 1 article
[sw41837]
 Federated Learning System and Benchmark for Graph Neural Networks. Graph Neural Network (GNN) research ... GNNs in learning distributed representations from graphstructured data. However, centralizing a massive amount...

HACTNet
 Referenced in 1 article
[sw39634]
 Hierarchical CelltoTissue Graph Neural Network for Histopathological Image Classification. Cancer diagnosis, prognosis ... level cellgraph, capturing cell morphology and interactions, a highlevel tissuegraph, capturing morphology ... tissue distribution. Further, a hierarchical graph neural network (HACTNet) is proposed to efficiently ... outperformed recent convolutional neural network and graph neural network approaches for breast cancer multiclass...

AliGraph
 Referenced in 1 article
[sw38083]
 AliGraph: A Comprehensive Graph Neural Network Platform. An increasing number of machine learning tasks require ... relationship among potentially billions of elements. Graph Neural Network (GNN) becomes an effective ... neural network for training and referencing. However, it is challenging to provide an efficient graph ... this paper, we present a comprehensive graph neural network system, namely AliGraph, which consists...

EigenGNN
 Referenced in 2 articles
[sw38084]
 Structure Preserving Plugin for GNNs. Graph Neural Networks (GNNs) are emerging machine learning models...

CoCoSUM
 Referenced in 1 article
[sw40142]
 Contextual Code Summarization with MultiRelational Graph Neural Network. Source code summaries are short natural ... Contextual Code Summarization with MultiRelational Graph Neural Networks. CoCoSUM first incorporates class names ... embeddings using a novel MultiRelational Graph Neural Network (MRGNN). Class semantic embeddings and class...

DeepSphere
 Referenced in 2 articles
[sw29899]
 Neural Networks (CNNs) to the sphere. We here model the discretised sphere as a graph ... data at multiple scales. The graph neural network model is based on ChebNet...

VGAER
 Referenced in 1 article
[sw41917]
 VGAER: Graph Neural Network Reconstruction based Community Detection. Community detection is a fundamental and important ... community detection algorithms based on graph neural networks, among which unsupervised algorithms are almost blank ... modularity information with network features, this paper proposes a Variational Graph AutoEncoder Reconstruction based community...

KPlexPool
 Referenced in 1 article
[sw42487]
 plex cover pooling for graph neural networks. Graph pooling methods provide mechanisms for structure reduction...