arXiv subjects

Félix Marcoccia

Publications and source records attributed to Félix Marcoccia.

3 recordsLinked to original sources

arXiv2026

Escaping Oversquashing: Addressable and Support-Aware Global Memory for Message Passing Networks

Virtual nodes are a natural tool against oversquashing: they replace long message-passing paths by a two-hop global route. But when many nodes share one global state, that shortcut can become a bottleneck itself. We study two properties of this global memory. First, addressability: under constant-margin address codes and a nonlinearity that amplifies this margin, multiplicative write/read maps provide $M$ selectable memory rows with only $O(\log M)$ address-code dimensions. Cross-attention slots and a constrained $ELU+1$ bilinear memory both satisfy these conditions. Second, support awareness: normalized cross-attention has no self-key for a latent query to use as a reference. A learned private anchor supplies this reference, keeps the read bounded, and exposes the strength of the matching source mass. We demonstrate the merits of such properties on several instances of Two-Radius and Tree-NeighborsMatch: both addressable realizations solve the controlled tasks through depth $5$, where pooled VNs of comparable or larger size reach about $10.6\%$.

cs.LG↗

arXiv2026

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

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.

cs.SI↗

arXiv2024

Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens

This work introduces Cross-Attentive Modulation (CAM) tokens, which are tokens whose initial value is learned, gather information through cross-attention, and modulate the nodes and edges accordingly. These tokens are meant to improve the global awareness of link predictions models which, based on graph neural networks, can struggle to capture graph-level features. This lack of ability to feature high level representations is particularly limiting when predicting multiple or entire sets of links. We implement CAM tokens in a simple attention-based link prediction model and in a graph transformer, which we also use in a denoising diffusion framework. A brief introduction to our toy datasets will then be followed by benchmarks which prove that CAM token improve the performance of the model they supplement and outperform a baseline with diverse statistical graph attributes.

cs.SI↗