3 papers
cs.AI2026
Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking
Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda +3
Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.…
cs.LG2026
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of…
cs.LG2026
GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
Riccardo Porcedda, Francesca Chiaromonte, Fabrizio Lillo +1
Link prediction (inferring missing or future connections between nodes in a graph) is a fundamental problem in network science with widespread applications in, e.g., biological sys…