14 citations · 36 across the 15 of their papers we have counts for
10 papers · 1 filter
Concept Graph Convolutions: Message Passing in the Concept Space
Lucie Charlotte Magister, Pietro Lio
The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks via concept-based explanation…
Subgraph Concept Networks: Concept Levels in Graph Classification
Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1
The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based…
Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts
Shiye Su, Iulia Duta, Lucie Charlotte Magister +1
Hypergraph neural networks are a class of powerful models that leverage the message passing paradigm to learn over hypergraphs, a generalization of graphs well-suited to describing…
Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero +2
Graph neural networks (GNNs) have led to major breakthroughs in a variety of domains such as drug discovery, social network analysis, and travel time estimation. However, they lack…
Interpretable Graph Networks Formulate Universal Algebra Conjectures
Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin +4
The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Yet, the use of A…
SHARCS: Shared Concept Space for Explainable Multimodal Learning
Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister +2
Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given mod…
GCI: A (G)raph (C)oncept (I)nterpretation Framework
Dmitry Kazhdan, Botty Dimanov, Lucie Charlotte Magister +3
Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks. An important challen…
Encoding Concepts in Graph Neural Networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan +5
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, how…
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis
Han Xuanyuan, Pietro Barbiero, Dobrik Georgiev +2
Graph neural networks (GNNs) are highly effective on a variety of graph-related tasks; however, they lack interpretability and transparency. Current explainability approaches are t…
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh +1
While graph neural networks (GNNs) have been shown to perform well on graph-based data from a variety of fields, they suffer from a lack of transparency and accountability, which h…