Open Calls

Call for Posters:

The meetup will host a local poster session (independent from the main event). We welcome posters from areas broadly related to learning on graphs and geometry. Poster abstracts must be submitted through the dedicated Google Form link using LoG LaTeX style files (download or Overleaf template). Submitted posters will be selected by the local organizing team.

Participation in the poster session is restricted to registered participants: Posters will not be considered if the respective author is not registerd for the event.

Call for Open Talks:

We are planning a session of open talks (a few minutes presentation) on general topics and open problems related to learning on graphs and geometry. These talks will be held in a single session on the morning of 11/29. Talks abstracts must be submitted through the open talks submission form. In the case that the number of applications should exceed the number of talks that can be included in the session, the organizing committee will reserve the right to select a limited number of talks.

More information on timing will be released as the schedule of the main conference is made known.

Registration:

Those who are interested in partecipating to the event must register to the specific EventBrite link. Speakers and poster presenter must register too.

Important dates:

Submission deadline (both calls): October 29, 2023

Final decision: November 19, 2023

Subject Areas:

We therefore invite submissions on theoretical aspects, algorithms and methods, and applications of the following (non-exhaustive) list of areas:

• Expressive Graph Neural Networks

• GNN architectures (transformers, new positional encodings, …)

• Equivariant architectures

• Statistical theory on graphs

• Causal inference (structural causal models, …)

• Algorithmic reasoning

• Geometry processing

• Robustness and adversarial attacks on graphs

• Combinatorial Optimization and Graph Algorithms

• Graph Kernels

• Graph Signal Processing/Spectral Methods

• Graph Generative Models

• Scalable Graph Learning Models and Methods

• Graphs for Recommender Systems

• Graph/Geometric ML for Computer Vision

• Knowledge Graphs

• Graph ML for Natural Language Processing

• Graph/Geometric ML for Molecules (molecules, proteins, drug discovery, …)

• Graph ML for Security

• Graph ML for Health

• Graph/Geometric ML for Physical sciences

• Graph ML Platforms and Systems

• Self-supervised learning on graphs

• Trustworthy graph ML (fairness, privacy, …)

• Graph/Geometric ML Infrastructures (datasets, benchmarks, libraries, …)

We welcome many kinds of posters, including relevant work that has been previously published