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
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
Minhao Zou, Zhongxue Gan, Yutong Wang, Junheng Zhang, Dongyan Sui, Chun Guan, Siyang Leng
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks
Nicola Rares Franco, Stefania Fresca, Filippo Tombari, Andrea Manzoni
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
Vasiliy Usatyuk, Sergey Egorov, Denis Sapozhnikov
Dynamic algorithms for k-center on graphs
Emilio Cruciani, Sebastian Forster, Gramoz Goranci, Yasamin Nazari, Antonis Skarlatos
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm
Ali Chaouche, Menouar Boulif
Curvature-based Clustering on Graphs
Yu Tian, Zachary Lubberts, Melanie Weber
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Longfeng Wu, Bowen Lei, Dongkuan Xu, Dawei Zhou