3 citations · 5 across the 2 of their papers we have counts for
4 papers
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
Hugo Attali, Nathalie Pernelle, Davide Buscaldi +1
Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where inform…
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
Hugo Attali, Davide Buscaldi, Nathalie Pernelle +1
Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where inform…
Knowledge Graph Refinement based on Triplet BERT-Networks
Armita Khajeh Nassiri, Nathalie Pernelle, Fatiha Sais +1
Knowledge graph embedding techniques are widely used for knowledge graph refinement tasks such as graph completion and triple classification. These techniques aim at embedding the…
The sameAs Problem: A Survey on Identity Management in the Web of Data
Joe Raad, Nathalie Pernelle, Fatiha Saïs +2
In a decentralised knowledge representation system such as the Web of Data, it is common and indeed desirable for different knowledge graphs to overlap. Whenever multiple names are…