• 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]
  • Graph-Centric, Highly-Performant 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 graph-level 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 ... Python-based 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 graph-structured...
  • CogDL

  • Referenced in 2 articles [sw37740]
  • several real-world applications such as social network analysis and large-scale 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 graph-based 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 Model-Level 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 graph-structured data. However, centralizing a massive amount...
  • HACT-Net

  • Referenced in 1 article [sw39634]
  • Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification. Cancer diagnosis, prognosis ... level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology ... tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently ... outperformed recent convolutional neural network and graph neural network approaches for breast cancer multi-class...
  • 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...
  • Eigen-GNN

  • Referenced in 2 articles [sw38084]
  • Structure Preserving Plug-in for GNNs. Graph Neural Networks (GNNs) are emerging machine learning models...
  • CoCoSUM

  • Referenced in 1 article [sw40142]
  • Contextual Code Summarization with Multi-Relational Graph Neural Network. Source code summaries are short natural ... Contextual Code Summarization with Multi-Relational Graph Neural Networks. CoCoSUM first incorporates class names ... embeddings using a novel Multi-Relational 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...