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
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph
Vladimir Ershov
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
Yunchong Song, Chenghu Zhou, Xinbing Wang, Zhouhan Lin
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network
Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, Huawei Shen, Xueqi Cheng