By integrating graph neural networks with energy-based models, our approach captures intricate fault ... The results provide substantial technical support for the intelligent diagnosis of construction ...

Graph Neural Networks (GNNs) and network embedding techniques have emerged as powerful tools for analyzing and interpreting complex data structures represented as graphs. These methods are ...

the authors present a comprehensive overview on Graph Neural Networks (GNNs) for Natural Language Processing. They propose a new taxonomy of GNNs for NLP, which systematically organizes existing ...

MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn ...

Abstract: Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods ...

What hopes do architects have for artificial intelligence? For our 1/2.2025 issue, we asked around 20 experts. Thomas ...

By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning Network Want to learn ...