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cs.LG2025
Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic +1
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMem…
cs.LG2025
GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring
Celia Rubio-Madrigal, Adarsh Jamadandi, Rebekka Burkholz
Maximizing the spectral gap through graph rewiring has been proposed to enhance the performance of message-passing graph neural networks (GNNs) by addressing over-squashing. Howeve…
cs.LG2024
Spectral Graph Pruning Against Over-Squashing and Over-Smoothing
Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz
Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former r…