ScoreCAM GNN: une explication optimale des réseaux profonds sur graphes
This addresses the need for explainability in graph-based deep learning, which is crucial for real-world applications, but appears incremental as it builds on existing methods.
The paper tackles the problem of explaining deep networks on graphs by proposing a method that is more optimal, lighter, consistent, and better exploits graph topology than state-of-the-art methods, though no concrete numbers are provided.
The explainability of deep networks is becoming a central issue in the deep learning community. It is the same for learning on graphs, a data structure present in many real world problems. In this paper, we propose a method that is more optimal, lighter, consistent and better exploits the topology of the evaluated graph than the state-of-the-art methods.