Graph Reasoning Unleashed: GNNs in LLMs, High-Order Inference, and Healthcare
Graph Reasoning Unleashed: GNNs in LLMs, High-Order Inference, and Healthcare
Workshop Summary
The main goal of this workshop is two-fold:
Exploring the Role of Graph Neural Networks (GNNs) in Emerging Domains. We aim to discuss the growing potential of GNNs in critical areas such as healthcare applications and reasoning within Large Language Models (LLMs).
Shaping the Future of Graph Machine Learning: The workshop will serve as a platform to identify and explore future directions in GNN research, with a particular focus on modeling higher-order graph structures and integrating GNNs into the evolving landscape of LLMs.
This workshop on Graph Machine Learning brings together researchers and practitioners to exchange insights, present novel ideas, and foster collaboration at the intersection of graph learning, healthcare, and advanced reasoning systems
Scope and Topics of Interest
Graph machine learning with applications to health care and biomedical domains.
Retrieval Augment Generation with graph ML.
Topological deep learning
Unsupervised learning on graphs with LLMs Scope and Topics of Interest
Important Dates
Submission Deadline: Sept 30, 2025
Acceptance Notification: Oct 15, 2025
Cam-ready Submission: Nov 10, 2025
Workshop: Dec 11, 2025
Submission Link: https://shorturl.at/SRn4b
Invited Speakers
Name: Elvin Isufi
Title of the talk : Learning from Topologically-related Data.
Bio: Elvin is an Associate Professor of Graph Machine Learning at TU Delft, The Netherlands, and co-founder/codirector of AIdrolab. His research focuses on signal processing and machine learning for graphs and topological structures. An IEEE Senior Member, he has received the IEEE Signal Processing Society’s Best PhD Dissertation Award and several best paper awards at top venues such as ICASSP and Asilomar.
Organizers
Prof. Sandeep Kumar Dr. Ekta Srivastava Dr. Thummaluru Siddhartha Reddy Indian Institute of Technology Delhi Indian Institute of Technology Delhi Fujitsu Research
Prof. Manoj Kumar Dr. Mahesh Chandran
Indian Institute of Technology, Dhanbad Fujitsu Research