Graph Drawing
Graph drawing research focuses on efficiently representing and manipulating graph-structured data, aiming to optimize algorithms for tasks like pathfinding, substructure counting, and graph classification. Current research emphasizes developing novel algorithms, including those based on reinforcement learning, linear programming, and graph neural networks (GNNs), to improve computational efficiency and address challenges like heterophily and scalability in large graphs. These advancements have significant implications for diverse fields, enabling faster and more accurate analysis of complex networks in areas such as social sciences, robotics, and materials science.
Papers
Presentations are not always linear! GNN meets LLM for Document-to-Presentation Transformation with Attribution
Himanshu Maheshwari, Sambaran Bandyopadhyay, Aparna Garimella, Anandhavelu Natarajan
Graph neural networks informed locally by thermodynamics
Alicia Tierz, Iciar Alfaro, David González, Francisco Chinesta, Elías Cueto
Uncertainty for Active Learning on Graphs
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
Graph is all you need? Lightweight data-agnostic neural architecture search without training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunhen Jiang, Jianxi Gao
Tackling Graph Oversquashing by Global and Local Non-Dissipativity
Alessio Gravina, Moshe Eliasof, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schönlieb
Efficient Algorithms for Learning Monophonic Halfspaces in Graphs
Marco Bressan, Emmanuel Esposito, Maximilian Thiessen
Robustness of graph embedding methods for community detection
Zhi-Feng Wei, Pablo Moriano, Ramakrishnan Kannan
Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse