Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...

ABSTRACT: The nearly analytic discretization of the frequency-domain wave equation produces large-scale, sparse, and ill-conditioned linear system, which challenge conventional iterative solvers. To ...

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

1 Department of Computer Engineering, School of Engineering, The University of Jordan, Amman, Jordan. 2 Department of Data Science and Artificial Intelligence, Faculty of Information Technology, ...

Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...

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Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea ...

Abstract: Attribute graph clustering is a fundamental and challenging task in graph data mining, requiring the adequate utilization of both node attributes and graph structure. Recently, a series of ...