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
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty
Xiaoqing Huang, Andersen Ang, Kun Huang, Jie Zhang, Yijie Wang
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
YanMing Hu, Chuan Chen, BoWen Deng, YuJing Lai, Hao Lin, ZiBin Zheng, Jing Bian