5 papers
Fixed Aggregation Features Can Rival GNNs
Celia Rubio-Madrigal, Rebekka Burkholz
Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations. We challenge this view by introducing Fixed A…
Hyperbolic Aware Minimization: Implicit Bias for Sparsity
Tom Jacobs, Advait Gadhikar, Celia Rubio-Madrigal +1
Understanding the implicit bias of optimization algorithms is key to explaining and improving the generalization of deep models. The hyperbolic implicit bias induced by pointwise o…
When Shift Happens - Confounding Is to Blame
Abbavaram Gowtham Reddy, Celia Rubio-Madrigal, Rebekka Burkholz +1
Distribution shifts introduce uncertainty that undermines the robustness and generalization capabilities of machine learning models. While conventional wisdom suggests that learnin…
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…
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…