From the 1 of 5 linked papers with an AI index.
5 papers
Explaining Temporal Graph Neural Networks via Feature-induced Information Flow
Ping Xiong, Thomas Schnake, Klaus-Robert Müller +1
The paper introduces an attribution method that explains temporal graph neural networks by quantifying information flow through both event embeddings and event-induced variables, i…
Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
Ping Xiong, Thomas Schnake, Grégoire Montavon +2
To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also exam…
Relevant Walk Search for Explaining Graph Neural Networks
Ping Xiong, Thomas Schnake, Michael Gastegger +3
Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise rel…
Efficient Higher-order Subgraph Attribution via Message Passing
Ping Xiong, Thomas Schnake, Grégoire Montavon +2
Explaining graph neural networks (GNNs) has become more and more important recently. Higher-order interpretation schemes, such as GNN-LRP (layer-wise relevance propagation for GNN)…
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer +5
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abst…