Connectionists: [CFP] Graph Neural Networks for Real-World Data

[Antonino Staiano]

Dear colleagues,

we are pleased to invite you to submit your original contributions to

Special Session: Graph Neural Networks for Real-World Data
which will take place at IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024 (IEEE EAIS 2024), to be held in Madrid, Spain, on 23–24 May, 2024.


Special Session website: https://sites.google.com/icar.cnr.it/gnn-for-rw-data/home
Paper submission deadline: Feb 15, 2024


Call for paper
Graphs are a powerful tool for the analysis and depiction of real-world data due to their ability to capture intricate relationships and structures inherent in many domains. These structures are often graph-like, and the interaction among various sources of information can be effectively represented using nodes and links. The application of the computational versatility of neural architectures to graphs has resulted in the development of powerful computational models, notably Graph Neural Networks (GNN). These models excel in exploiting the inherent information present in a graph structure. GNNs offer a versatile framework for various computations, including but not limited to node classification, graph classification, and link prediction. These computations play a crucial role in solving supervised and unsupervised classification problems.
Moreover, GNNs prove their adaptability by accommodating information that doesn't conform to traditional grid-like structures. This flexibility allows data to be cast into graphs, considering a topology derived from specific features and adjacency matrices. By doing so, GNNs become applicable to a broader range of data types and structures. The overarching goal of this special session is to provide a forum for the presentation and discussion of original papers and reviews on the latest methods involving GNNs. These methods are specifically tailored for the analysis of diverse real-world data types. Examples of such data include environmental, biomedical, and social network data. By exploring the application of GNNs across different domains, we aim to foster a deeper understanding of their capabilities and potential contributions to advancing data analysis and representation in various fields.

Topics:

General GNN-based architectures
Representation Learning by GNNs
Explainability in GNNs
GNNs in Computer vision and Image Processing
GNNs in Natural Language Processing
Processing of Biological data with GNNs
GNNs for Environmental Monitoring


Organizing Committee
Giosué Lo Bosco, Department of Mathematics and Computer Science of the University of Palermo, Italy.
Salvatore Calderaro, Department of Mathematics and Computer Science of the University of Palermo, Italy.
Riccardo Rizzo, Institute for High Performance Computing and Networking, CNR, Italy
Antonino Staiano, Department of Science and Technology, University of Naples Parthenope, Italy
Filippo Vella, Institute for High Performance Computing and Networking, CNR, Italy


Antonino Staiano


Antonino Staiano, PhD
Associate Professor
Department of Science and Technology
University of Naples Parthenope,
Centro Direzionale Isola C4, 80143, Napoli, Italy
Room 429, 4th Floor (North side)
Email: antonino.staiano at uniparthenope.it<mailto:antonino.staiano at uuniparthenope.it>
Phone : +39 081 5476520

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