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
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements
Peter A. Zachares, Vahan Hovhannisyan, Alan Mosca, Yarin Gal
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Ziyue Li, Hao Yan, Chen Zhang, Lijun Sun, Wolfgang Ketter, Fugee Tsung
ControlLLM: Augment Language Models with Tools by Searching on Graphs
Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao, Erfei Cui, Ziheng Li, Xizhou Zhu, Lewei Lu, Qifeng Chen, Yu Qiao, Jifeng Dai, Wenhai Wang
Learning the dynamics of a one-dimensional plasma model with graph neural networks
Diogo D Carvalho, Diogo R Ferreira, Luis O Silva
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter