mlsanityData to 14 Oct 2026

Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence

arXiv 2407.15320 ↗First posted 7 Jul 2024cs.DC cs.AI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them -- GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning,2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 186 of its 405 references that are in the map. First posted on arXiv 7 Jul 2024.

Builds on

186 of 405 references.

References · 186

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2017Graph Attention Networkscited by 8,923
  5. 2017Inductive Representation Learning on Large Graphscited by 6,320
  6. 2018How Powerful are Graph Neural Networks?cited by 4,393
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  9. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  10. 2020Knowledge Distillation: A Surveycited by 1,603
  11. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  12. 2019Simplifying Graph Convolutional Networkscited by 1,477
38 more
  1. 2016Variational Graph Auto-Encoderscited by 1,382
  2. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 713
  6. 2020Heterogeneous Graph Transformercited by 582
  7. 2019Graph Transformer Networkscited by 523
  8. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  9. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  10. 2018Deep Learning on Graphs: A Surveycited by 393
  11. 2019Provably Powerful Graph Networkscited by 389
  12. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  13. 2019On the Efficacy of Knowledge Distillationcited by 381
  14. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  15. 2019Multi-Label Image Recognition with Graph Convolutional Networkscited by 322
  16. 2015LSTM: A Search Space Odysseycited by 316
  17. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  18. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  19. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  20. 2018Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluidscited by 220
  21. 2019Learning a Neural Solver for Multiple Object Trackingcited by 198
  22. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 152
  23. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  24. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  25. 2020Distilling Knowledge from Graph Convolutional Networkscited by 129
  26. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  27. 2021An Empirical Study of Graph Contrastive Learningcited by 113
  28. 2020SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloudcited by 112
  29. 2022Transformer for Graphs: An Overview from Architecture Perspectivecited by 107
  30. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  31. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  32. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 96
  33. 2019Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learningcited by 94
  34. 2020A Systematic Survey on Deep Generative Models for Graph Generationcited by 94
  35. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  36. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  37. 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
  38. 2019Graph Neural Networks for Decentralized Multi-Robot Path Planningcited by 86

50 most cited of 186 All 186 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devicescited by 15
  2. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  3. 2026Co-Optimizing Graph Sparsification and Approximate Computing for Energy-Efficient FPGA-Based GCN Inferencecited by 0