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<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Dear colleagues,
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">we are pleased to invite you to submit your original contributions to
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Special Session: Graph Neural Networks for Real-World Data<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Special Session website: https://sites.google.com/icar.cnr.it/gnn-for-rw-data/home
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<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Paper submission deadline: Feb 15, 2024<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Call for paper<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">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.
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Topics:<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">General GNN-based architectures<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Representation Learning by GNNs<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Explainability in GNNs<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">GNNs in Computer vision and Image Processing<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">GNNs in Natural Language Processing<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Processing of Biological data with GNNs<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">GNNs for Environmental Monitoring<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Organizing Committee<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Giosué Lo Bosco, Department of Mathematics and Computer Science of the University of Palermo, Italy.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Salvatore Calderaro, Department of Mathematics and Computer Science of the University of Palermo, Italy.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Riccardo Rizzo, Institute for High Performance Computing and Networking, CNR, Italy<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Antonino Staiano, Department of Science and Technology, University of Naples Parthenope, Italy<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Filippo Vella, Institute for High Performance Computing and Networking, CNR, Italy<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Antonino Staiano<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Antonino Staiano, PhD<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Associate Professor<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Department of Science and Technology<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">University of Naples Parthenope,
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Centro Direzionale Isola C4, 80143, Napoli, Italy<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:11.0pt">Room 429, 4<sup>th</sup> Floor (North side)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="FR" style="font-size:11.0pt">Email: <a href="mailto:antonino.staiano@uuniparthenope.it">
<span style="color:#0563C1">antonino.staiano@uniparthenope.it</span></a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="FR" style="font-size:11.0pt">Phone : +39 081 5476520<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="FR"><o:p> </o:p></span></p>
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