mlsanityData to 14 Oct 2026

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

arXiv 2604.20483 ↗First posted 22 Apr 2026cs.NI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structure and the connection features. We use sliding-windows to split the network traffic in equal-sized heterogeneous bidirectional graphs containing IP, Port, and Connection nodes. We then use the GNN to model the evolution of the graph structure and the connection features. Our approach shows superior results when identifying the Port and IP to which connections attach, while feature reconstruction remains competitive with strong forecasting baselines. Overall, our work showcases the use of GNNs for per-flow NetFlow prediction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 41 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

21 of 41 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  5. 2017Inductive Representation Learning on Large Graphscited by 6,320
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  7. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  9. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  10. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  11. 2016Variational Graph Auto-Encoderscited by 1,382
  12. 2022Transformers in Time Series: A Surveycited by 497
9 more
  1. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  2. 2018A System for Massively Parallel Hyperparameter Tuningcited by 202
  3. 2022Heterogeneous Graph Masked Autoencoderscited by 47
  4. 2021Unveiling the potential of Graph Neural Networks for robust Intrusion Detectioncited by 14
  5. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  6. 2024Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Networkcited by 7
  7. 2018Towards Fine Grained Network Flow Predictioncited by 1
  8. 2025m4: A Learned Flow-level Network Simulatorcited by 1
  9. 2025AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoderscited by 1

Built on by

Not cited yet.