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
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance
Andrea Cavallo, Claas Grohnfeldt, Michele Russo, Giulio Lovisotto, Luca Vassio
Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networks
Anton Raskovalov, Nikita Gabdullin, Vasily Dolmatov
Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
Adam Tonks, Trevor Harris, Bo Li, William Brown, Rebecca Smith
Towards dynamic stability analysis of sustainable power grids using graph neural networks
Christian Nauck, Michael Lindner, Konstantin Schürholt, Frank Hellmann