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
Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero, Pietro Liò, Nikola Simidjievski
A latent linear model for nonlinear coupled oscillators on graphs
Agam Goyal, Zhaoxing Wu, Richard P. Yim, Binhao Chen, Zihong Xu, Hanbaek Lyu
Three-dimensional granular flow simulation using graph neural network-based learned simulator
Yongjin Choi, Krishna Kumar
Classification of developmental and brain disorders via graph convolutional aggregation
Ibrahim Salim, A. Ben Hamza
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning
Thomas Bonald, Nathan de Lara