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.
756papers
Papers - Page 30
June 25, 2023
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June 19, 2023
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement
Lucas Potin, Rosa Figueiredo, Vincent Labatut, Christine LargeronUnsupervised Framework for Evaluating and Explaining Structural Node Embeddings of Graphs
Ashkan Dehghan, Kinga Siuta, Agata Skorupka, Andrei Betlen, David Miller, Bogumil Kaminski, Pawel Pralat
June 14, 2023
June 12, 2023
Efficiently Learning the Graph for Semi-supervised Learning
Dravyansh Sharma, Maxwell JonesCARL-G: Clustering-Accelerated Representation Learning on Graphs
William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao, Neil Shah, Evangelos E. PapalexakisA Graph Transformer-Driven Approach for Network Robustness Learning
Yu Zhang, Jia Li, Jie Ding, Xiang Li