Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over …
Graph-based message-passing neural networks (MPNNs) have achieved remarkable success in both node and graph-level learning tasks. However, several identified problems …
L Fesser, M Weber - arXiv preprint arXiv:2311.14864, 2023 - arxiv.org
Structural and Positional Encodings can significantly improve the performance of Graph Neural Networks in downstream tasks. Recent literature has begun to systematically …
M Weber - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
A key challenge in Machine Learning (ML) is the identification of geometric structure in high- dimensional data. Most algorithms assume that data lives in a high-dimensional vector …
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and …
D Shi, L Lin, A Han, Z Wang, Y Guo, J Gao - arXiv preprint arXiv …, 2024 - arxiv.org
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between …
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), ie neural …
Graph neural networks (GNNs) take as input the graph structure and the feature vectors associated with the nodes. Both contain noisy information about the labels. Here we …
A Feng, M Weber - arXiv preprint arXiv:2407.04236, 2024 - arxiv.org
Graph Machine Learning often involves the clustering of nodes based on similarity structure encoded in the graph's topology and the nodes' attributes. On homophilous graphs, the …