Connectionists: [CFP] Special Session: Foundation and Generative Models for Graphs @ESANN2025

[Davide Rigoni]

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CALL FOR PAPERS (ESANN 2025)
European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learninghttps://www.esann.org/
More details on the Special Session "Foundation and Generative Models
for Graphs":https://www.esann.org/ESANN2025specialsessions#graphs
Bruges, Belgium, 23-25 April 2025
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****** Call For Papers ******
Graphs are versatile abstractions that model complex systems of
interacting entities, where interactions imply functional and/or
structural dependencies among them. Molecular compounds and social
networks are two particularly relevant instances of graph-structured
data: the former can be viewed as a system of interacting atoms, where
chemical bonds are influenced by factors like inter-atomic distances
and atomic energies/forces depend on long-range electrostatic
interactions. Instead, social networks manifest in terms of user-user
and user-content interactions, where content is multimodal and
includes pictures, movies, and songs. Other practical examples include
the encoding of symmetries and constraints in combinatorial
optimization problems, which serve as proxies for apriori
knowledge.The field of deep learning for graph-structured data focuses
on adapting deep learning techniques, such as convolutional operators,
to the analysis and processing of graphs. Recent advancements in
foundation and generative models for non-graph data have significantly
impacted businesses, with an unprecedented speed of adoption in
production environments. It is therefore natural to ask ourselves
whether such advances can be readily transferred to the domain of
graphs, and what the best way to do so would be, opening new avenues
for research and applications. In this respect, there are high
expectations in areas such as drug discovery, where the ability to
generate molecular constraint-preserving graphs with desired
properties might reduce the humongous amount of money and compute time
needed to screen candidate drugs. This 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.

****** Important Dates ******
Paper Submissions: 20 November 2024
Paper Acceptance Notifications: 24 January 2025
Conference: 23-25 April 2025

****** Topics ******
This special session aims to gather valuable contributions and new
findings in the field of deep learning for graphs. It will also
emphasize the integration of foundation and generative models to
enhance the creation and manipulation of graph-structured data,
unlocking new possibilities in areas such as molecular and material
design. In particular, we look for contributions in the following
areas:
-> Architectures for foundation models operating on graphs;
-> Graph generation (e.g., probabilistic models, variational
autoencoders, normalizing flow, diffusion models);
adversarial learning, etc.)ù
-> Graph representation learning;
-> Graph structure learning and relational inference;
-> Graph coarsening and pooling in graph neural networks;
-> Theory of graph neural networks (e.g., expressive power,
learnability, negative results);
-> Learning on complex graphs (e.g., dynamic graphs, heterogeneous
graphs, and spatio-temporal data);
-> Anomaly and change detection in graph data;
-> Randomized neural networks for graphs (e.g., reservoir computing);
-> Recurrent, recursive, and contextual models;
-> Scalability, data efficiency, and training techniques of graph
neural networks;
-> Tensor methods for structured data;
-> Graph datasets and benchmarks.

****** Session Organisers ******
-> Davide Rigoni (University of Padua)
-> Luca Pasa (University of Padua)
-> Federico Errica (NEC Laboratories Europe)
-> Daniele Zambon (Swiss AI Lab IDSIA, Università della Svizzera italiana)
-> Davide Bacciu (University of Pisa)
-> Stefano Moro (University of Padua)
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