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
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
Raffaele Marino, Lorenzo Buffoni, Bogdan Zavalnij
Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs
Fujing Xie, Sören Schwertfeger
A Mixed-Integer Conic Program for the Moving-Target Traveling Salesman Problem based on a Graph of Convex Sets
Allen George Philip, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
Ang Li, Qiangchao Chen, Yiquan Wu, Ming Cai, Xiang Zhou, Fei Wu, Kun Kuang
A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics
Emanuel Pfeffer, Michael Waßmer, Yee-Ying Cung, Roger Wolf, Ulrich Husemann
GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
Le Cheng, Peican Zhu, Keke Tang, Chao Gao, Zhen Wang
LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang, Jiawei Sun, Jie Li, Chentao Wu
Information Flow Routes: Automatically Interpreting Language Models at Scale
Javier Ferrando, Elena Voita