4 citations · 4 across the 1 of their papers we have counts for
2 papers
cs.LG2020
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux +3
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some e…
cs.DS2015★ 4 cited
Graph edit distance : a new binary linear programming formulation
Julien Lerouge, Zeina Abu-Aisheh, Romain Raveaux +2
Graph edit distance (GED) is a powerful and flexible graph matching paradigm that can be used to address different tasks in structural pattern recognition, machine learning, and da…