3 papers
cs.LG2025
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Jie Yang, Yuwen Wang, Kaixuan Chen +6
Interpretable Graph Neural Networks (GNNs) aim to reveal the underlying reasoning behind model predictions, attributing their decisions to specific subgraphs that are informative.…
cs.LG2024
Simple Graph Condensation
Zhenbang Xiao, Yu Wang, Shunyu Liu +3
The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed…
cs.SI2024
Disentangled Condensation for Large-scale Graphs
Zhenbang Xiao, Yu Wang, Shunyu Liu +4
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the orig…