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
Targeted Therapy in Data Removal: Object Unlearning Based on Scene Graphs
Chenhan Zhang, Benjamin Zi Hao Zhao, Hassan Asghar, Dali Kaafar
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
Amirabbas Afzali, Hesam Hosseini, Mohmmadamin Mirzai, Arash Amini
Graph Pooling with Local Cluster Selection
Yizhu Chen
A Data-Driven Approach to Dataflow-Aware Online Scheduling for Graph Neural Network Inference
Pol Puigdemont, Enrico Russo, Axel Wassington, Abhijit Das, Sergi Abadal, Maurizio Palesi
Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs
Zhaobin Mo, Qingyuan Liu, Baohua Yan, Longxiang Zhang, Xuan Di
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
Benchmarking Positional Encodings for GNNs and Graph Transformers
Florian Grötschla, Jiaqing Xie, Roger Wattenhofer
Graph as a feature: improving node classification with non-neural graph-aware logistic regression
Simon Delarue, Thomas Bonald, Tiphaine Viard
A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs
Zixin Liu, Ji Zhang, Yiran Ding