mlsanityData to 7 Oct 2026

Efficient Graph Deep Learning in TensorFlow with tf_geometric

arXiv 2101.11552 ↗First posted 27 Jan 2021cs.LG stat.MLcited by 17

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Abstract (arXiv)

We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as transductive node classification, inductive node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models for different tasks. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced graph deep learning tasks such as graph meta-learning. The APIs of tf_geometric are friendly, and they are suitable for both beginners and experts. In this paper, we first present an overview of tf_geometric's framework. Then, we conduct experiments on some benchmark datasets and report the performance of several popular GNN models implemented by tf_geometric.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

27 of 40 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2019Simplifying Graph Convolutional Networkscited by 1,470
  6. 2018Deep Graph Infomaxcited by 1,101
  7. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  8. 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
  9. 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
  10. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  11. 2019Graph Neural Networks for Social Recommendationcited by 711
  12. 2019Self-Attention Graph Poolingcited by 421
15 more
  1. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 241
  2. 2015Order Matters: Sequence to sequence for setscited by 236
  3. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
  4. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  5. 2019Relation-Aware Graph Attention Network for Visual Question Answeringcited by 119
  6. 2019Hierarchical Graph Network for Multi-hop Question Answeringcited by 83
  7. 2020Location-aware Graph Convolutional Networks for Video Question Answeringcited by 80
  8. 2017Graph Convolutional Encoders for Syntax-aware Neural Machine Translationcited by 70
  9. 2019DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationcited by 59
  10. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  11. 2020Learning to Hash with Graph Neural Networks for Recommender Systemscited by 31
  12. 2018Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networkscited by 30
  13. 2020Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Predictioncited by 21
  14. 2021ADiag: Graph Neural Network Based Diagnosis of Alzheimer's Diseasecited by 3
  15. 2019Visual Query Answering by Entity-Attribute Graph Matching and Reasoningcited by 2

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Citing papers per year: 2021: 3; 2022: 8; 2023: 3; 2024: 3.

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