Arun Viswanathan, Nancy Feldman, et al.
IEEE Communications Magazine
Deep learning’s performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.
Arun Viswanathan, Nancy Feldman, et al.
IEEE Communications Magazine
A. Gupta, R. Gross, et al.
SPIE Advances in Semiconductors and Superconductors 1990
Joel L. Wolf, Mark S. Squillante, et al.
IEEE Transactions on Knowledge and Data Engineering
Leo Liberti, James Ostrowski
Journal of Global Optimization