3 papers
cs.LG2026
Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov
Graph alignment, the problem of identifying corresponding nodes across multiple graphs, is fundamental to numerous applications. Most existing unsupervised methods embed node featu…
cs.LG2026
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Erkan Turan, Gaspard Abel, Maysam Behmanesh +2
Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from oversmoothing, where node features co…
cs.LG2024
Smoothed Graph Contrastive Learning via Seamless Proximity Integration
Maysam Behmanesh, Maks Ovsjanikov
Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives using a selection process that typically relies on establishing…