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cs.LG2026
Training-Free Cross-Architecture Merging for Graph Neural Networks
Rishabh Bhattacharya, Vikaskumar Kalsariya, Naresh Manwani
Model merging has emerged as a powerful paradigm for combining the capabilities of distinct expert models without the high computational cost of retraining, yet current methods are…
cs.LG2026
EdgeMask-DG*: Learning Domain-Invariant Graph Structures via Adversarial Edge Masking
Rishabh Bhattacharya, Naresh Manwani
Structural shifts pose a significant challenge for graph neural networks, as graph topology acts as a covariate that can vary across domains. Existing domain generalization methods…
cs.LG2025
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
Rishabh Bhattacharya, Hari Shankar, Vaishnavi Shivkumar +1
The growing adoption of Graph Neural Networks (GNNs) in high-stakes domains like healthcare and finance demands reliable explanations of their decision-making processes. While inhe…