Author

Constantinos Antoniou

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Jul 2026

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

This research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding (GUIDED), which enables highly parameter-efficient domain adaptation for inter-network transfer learning without artificial input homogenization, establishing a robust foundation for truly induc...

Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou · arXiv.org · 0 citations

Open access Aug 2026

GSPINN: A Graph Sequential Physics-Informed Surrogate for Trip Travel Time Prediction

This work shows that embedding physically meaningful structure into learning objectives is an effective strategy for traffic surrogate modeling, yielding models that maintain competitive predictive accuracy while substantially improving directional behavioral consistency.

Blessing Itoro Afolayan, Arka Ghosh, Santhanakrishnan Narayanan et al. · Communications in Transporta... · 0 citations