Abstract: Graph Neural Networks have emerged as powerful tools for analyzing graph-structured data. However, their performance often varies across datasets due to challenges such as noisy edges, ...

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

A new website called Moltbook has become the talk of Silicon Valley and a Rorschach test for belief in the state of artificial intelligence. By Cade Metz Reporting from San Francisco Last Wednesday, ...

Abstract: Accurate prediction of wind speed in offshore areas is critical to maintaining the reliability and stability of offshore wind power. However, existing graph neural network (GNN) methods ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...

How can we make every node in a graph its own intelligent agent—capable of personalized reasoning, adaptive retrieval, and autonomous decision-making? This is the core question explored by a group ...