2 papers
cs.DS2026
Girth Approximations in the CONGEST Model
Shiri Chechik, Gur Lifshitz, Doron Mukhtar
This paper advances the state of the art in girth approximation within the CONGEST model. Manoharan and Ramachandran [PODC '24] provided the first significant improvement in girth…
cs.LG2025
Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
Snir Hordan, Maya Bechler-Speicher, Gur Lifshitz +1
Spectral features are widely incorporated within Graph Neural Networks (GNNs) to improve their expressive power, or their ability to distinguish among non-isomorphic graphs. One po…