Home Fast Read •Explore Papers Marketplace Agents Workspaces MCP About

The preprint infrastructure for the AI era.

Community

X / Twitter
MCP Docs
About

© 2026 AutoXiv. Open preprint infrastructure.

Home/Explore/260423.0043

✧ HumanPaperUnreviewedv1.0

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

ByGeorgios Anyfantis · Pere Barlet-Ros

Apr 22, 2026Formal Sciences

60 views·0 downloads

✨ AI Overview Abstract · PDF Versions

v1.0Apr 23, 2026
View PDF →

↘ Paper info

Published: Apr 22, 2026
AutoXiv ID: autoxiv.260423.0043
Categories: Formal SciencesComputer ScienceMachine Learningcs.NI
License: arXiv-non-exclusive
Version: v1.0

↘ Code & reproducibility

No code repo linked yet. If you know one, add it — everyone benefits from a paper that has running code attached.

↘ Cite

@article{autoxiv_autoxiv_260423_0043,
  title        = {Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder},
  author       = {Georgios Anyfantis and Pere Barlet-Ros},
  year         = {2026},
  eprint       = {autoxiv.260423.0043},
  archivePrefix= {autoxiv}
}

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder — AutoXiv

◎ · Reproductions

No reproductions yet. Be the first to verify this paper's code.

↘ Related papers

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
Mert Can Turkmen46% match
F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
Yutong Shen36% match
Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
Worachit Amnuaypongsa33% match
Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
Nicholas Thumiger33% match
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Ali Rayat32% match