Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …
graph-structured data. However, many real-world systems are dynamic in nature, since the …
Explaining the explainers in graph neural networks: a comparative study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …
(GNNs) have reached a widespread application in many science and engineering fields …
The expressive power of pooling in graph neural networks
FM Bianchi, V Lachi - Advances in neural information …, 2023 - proceedings.neurips.cc
Abstract In Graph Neural Networks (GNNs), hierarchical pooling operators generate local
summaries of the data by coarsening the graph structure and the vertex features …
summaries of the data by coarsening the graph structure and the vertex features …
A survey on explainability of graph neural networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have
gained significant attention and demonstrated remarkable performance in various domains …
gained significant attention and demonstrated remarkable performance in various domains …
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
F Mumuni, A Mumuni - Cognitive Systems Research, 2024 - Elsevier
We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
Interpretable neural-symbolic concept reasoning
Deep learning methods are highly accurate, yet their opaque decision process prevents
them from earning full human trust. Concept-based models aim to address this issue by …
them from earning full human trust. Concept-based models aim to address this issue by …
Factorized explainer for graph neural networks
Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. To open the black-box of these deep learning models, post …
learn from graph-structured data. To open the black-box of these deep learning models, post …
NCH-DDA: Neighborhood contrastive learning heterogeneous network for drug–disease association prediction
Exploring new therapeutic diseases for existing drugs plays an essential role in reducing
drug development costs. However, existing methods for predicting drug–disease association …
drug development costs. However, existing methods for predicting drug–disease association …
Generating explanations for conceptual validation of graph neural networks: An investigation of symbolic predicates learned on relevance-ranked sub-graphs
B Finzel, A Saranti, A Angerschmid, D Tafler… - KI-Künstliche …, 2022 - Springer
Abstract Graph Neural Networks (GNN) show good performance in relational data
classification. However, their contribution to concept learning and the validation of their …
classification. However, their contribution to concept learning and the validation of their …
State of the art and potentialities of graph-level learning
Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …