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
Alleviating Over-Smoothing via Aggregation over Compact Manifolds
Dongzhuoran Zhou, Hui Yang, Bo Xiong, Yue Ma, Evgeny Kharlamov
Decomposing heterogeneous dynamical systems with graph neural networks
Cédric Allier, Magdalena C. Schneider, Michael Innerberger, Larissa Heinrich, John A. Bogovic, Stephan Saalfeld
Self-Duplicating Random Walks for Resilient Decentralized Learning on Graphs
Maximilian Egger, Ghadir Ayache, Rawad Bitar, Antonia Wachter-Zeh, Salim El Rouayheb
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
Kai Guo, Zewen Liu, Zhikai Chen, Hongzhi Wen, Wei Jin, Jiliang Tang, Yi Chang
A review of graph neural network applications in mechanics-related domains
Yingxue Zhao, Haoran Li, Haosu Zhou, Hamid Reza Attar, Tobias Pfaff, Nan Li
Explaining Graph Neural Networks for Node Similarity on Graphs
Daniel Daza, Cuong Xuan Chu, Trung-Kien Tran, Daria Stepanova, Michael Cochez, Paul Groth
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
Yuhan Li, Peisong Wang, Xiao Zhu, Aochuan Chen, Haiyun Jiang, Deng Cai, Victor Wai Kin Chan, Jia Li
G-Adaptive mesh refinement -- leveraging graph neural networks and differentiable finite element solvers
James Rowbottom, Georg Maierhofer, Teo Deveney, Katharina Schratz, Pietro Liò, Carola-Bibiane Schönlieb, Chris Budd
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
Petr Anokhin, Nikita Semenov, Artyom Sorokin, Dmitry Evseev, Mikhail Burtsev, Evgeny Burnaev