Deep Learning for Graphs
Special Session @ IEEE WCCI 2024
Special Session @ IEEE WCCI 2024
Deep Learning for Graphs is a special session at the 2024 IEEE International Joint Conference on Neural Networks, World Congress on Computational Intelligence (WCCI), which will be held in Yokohama, Japan, June 30 - July 5, 2024.
Paper Submissions: January 15th, 2024
Paper Submissions EXTENDED: January 29th, 2024
Paper Acceptance Notifications: March 15, 2024
Conference: June 30 - July 5, 2024
The research field of deep learning for graphs studies the application of well-known deep learning concepts, such as convolution operators on images, to the processing of graph-structured data. Graphs are abstract objects that naturally represent interacting systems of entities, where interactions denote functional and/or structural dependencies between them.
Molecular compounds and social networks are the most common examples of such graphs: on the one hand, a molecule is seen as a system of interacting atoms, whose bonds depend, e.g., on their inter-atomic distance; on the other hand, a social network represents a vastly heterogeneous set of user-user interactions, as well as between users and items, like, pictures, movies and songs. Besides, graph representations are extremely useful in far more domains, for instance to encode symmetries and constraints of combinatorial optimization problems as a proxy of our a-priori knowledge. For these reasons, learning how to properly map graphs and their nodes to values of interest poses extremely important, yet challenging, research questions. This special session on graph learning will solicit recent advances that exploit various topics to benefit the solving of real-world problems.
The special session is an excellent opportunity for the machine learning community to gather together and host novel ideas, showcase potential applications, and discuss the new directions of this remarkably successful research field.
This session focuses on the broad spectrum of machine learning methods for structured and relational data, with a focus on deep representation learning. Theoretical and methodological papers are welcome from any of the following areas, including but not limited to:
Graph representation learning
Graph generation (probabilistic models, variational autoencoders, adversarial learning, etc.)
Graph learning and relational inference
Graph coarsening and pooling in graph neural networks
Graph kernels and distances
Theory of graph neural networks (e.g., expressive power, learnability, negative results)
Learning on complex graphs (e.g., dynamic graphs and heterogeneous graphs)
Deep learning for dynamic graphs and spatio-temporal data
Anomaly and change detection in graph data
Reservoir computing and randomized neural networks for graphs
Recurrent, recursive, and contextual models
Neural algorithmic reasoning
Relational reinforcement learning
Automatic graph machine learning
Scalability, data efficiency, and training techniques of graph neural networks
Tensor methods for structured data
Graph datasets and benchmarks
We also encourage application papers focused on but not limited to:
Bioinformatics (e.g., drug discovery and protein folding)
Cybersecurity (e.g., fraud detection)
Transportation Systems (e.g., traffic forecasting)
Recommender Systems (e.g., dynamic link prediction)
Graph Machine Learning Platforms and Systems
Computer Vision (e.g. point clouds)
Natural Language Processing
Go to the IEEE WCCI 2024 website and click on "Submit your paper".
You will be redirected to EDAS. Log into the system.
Select "IJCNN 2024 Special Session Papers"
Insert details of your paper and select the topic "Special Session: Deep Learning for Graphs"
Click on "Register Paper". Good Luck!!
Nicolò Navarin (University of Padua)
Davide Bacciu (University of Pisa)
Daniele Zambon (Swiss AI Lab IDSIA, Università della Svizzera italiana)
Federico Errica (NEC Laboratories Europe)
Daniele Castellana (University of Florence)
Luca Pasa (University of Padua)
Davide Rigoni (University of Padua)
Filippo Maria Bianchi (UiT the Arctic University of Norway)
For any enquire, please write to daniele.zambon [at] usi.ch or federico.errica [at] neclab.eu