Abstract: Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of ...
The methodology presented here is described in depth in Kalirad, A., & Sommer, R. J. (2025). Ecological graph theory: Simulating competition and coexistence on graphs ...
Introduction: Recent advancements in brain network analysis have greatly improved the diagnosis of neurodegenerative diseases. However, most existing studies rely on single-frequency EEG ...
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China Department of Physics, City University of Hong Kong, Kowloon 999077, Hong Kong China ...
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 ...
The study of geodetic numbers in graph theory represents a compelling fusion of abstract mathematical ideas with practical applications across network analysis, computational optimisation, and ...
Currently, our Graph Schema defaults to a simple template. However, new users are often unfamiliar with graphs, making schema construction challenging. We can simplify this process (semi-automatically ...
ABSTRACT: This paper contributes to the theoretical literature by analyzing the relationship between changes in sparsity and their impacts on financial networks with incomplete and random ...
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