NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies

Abstract:We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks. Directional antennas can yield high throughput but require globally consistent link decisions under sector, interference, connectivity, and half-duplex constraints. NetDiff improves global coherence with Absolute Cross-Attentive Modulation (ACAM) tokens, which provide permutation-invariant global signals and help the model match graph-level counts (e.g., density and sector usage). We also propose partial diffusion to update an existing topology with a small number of denoising steps, enabling fast reconfiguration under mobility. NetDiff reaches over 95 % of target performance with constant inference time, outperforms heuristic and omnidirectional baselines, and improves over a strong diffusion graph-transformer baseline in key metrics.

Social and Information Networks (cs.SI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2410.08238 [cs.SI]

(or arXiv:2410.08238v2 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2410.08238 Focus to learn more arXiv-issued DOI via DataCite

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