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 ...

Abstract: Neural architecture search (NAS ... most existing work rely on a manually designed search space defined by a directed acyclic graph (DAG), resulting in limited generalization capability and ...

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 ...

Abstract: Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work ...

AI models like artificial neural networks and language models help scientists solve a variety of problems, from predicting the 3D structure of proteins to designing novel antibiotics from scratch.

Common sense has been viewed as one of the hardest challenges in AI. That said, ChatGPT4 has acquired what some believe is an impressive sense of humanity. How is this possible? Listen to this week’s ...

On December 19, 1154, in Westminster Abbey in England, Henry FitzEmpress, an educated 21-year old, became the first man to dare to crown himself “King of England”. His aspiration was to create ...