The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].
Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications / A. D'Amelio, J. Lin, J.-. Ramel, R. Lanzarotti. - In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING. - ISSN 2168-6750. - 12:1(2024 Jan), pp. 122-125. [10.1109/TETC.2024.3374581]
Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications
A. D'AmelioPrimo
;J. LinSecondo
;R. Lanzarotti
Ultimo
2024
Abstract
The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can be effectively modeled as graphs, introducing a new representation learning paradigm. The significance of GNNs extends to several domains, including computer vision [3], [4], natural language processing [5], chemistry/biology [6], physics [7], traffic networks [8], and recommendation systems [9].File | Dimensione | Formato | |
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