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
Non-Clashing Teaching Maps for Balls in Graphs
Jérémie Chalopin, Victor Chepoi, Fionn Mc Inerney, Sébastien Ratel
Unifying over-smoothing and over-squashing in graph neural networks: A physics informed approach and beyond
Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
Sichao Fu, Qinmu Peng, Yang He, Baokun Du, Xinge You
Gotta match 'em all: Solution diversification in graph matching matched filters
Zhirui Li, Ben Johnson, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
M V Sai Prakash, Siddartha Reddy N, Ganesh Parab, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan