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
Accelerating discrete dislocation dynamics simulations with graph neural networks
Nicolas Bertin, Fei Zhou
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich
Graph neural networks for the prediction of molecular structure-property relationships
Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann