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

November 28, 2022

November 27, 2022

ReGrAt: Regularization in Graphs using Attention to handle class imbalance
Neeraja Kirtane, Jeshuren Chelladurai, Balaraman Ravindran, Ashish Tendulkar
Graph Drawing Human Attention Class Imbalance Node Classification Attention Network Graph Based Learning

November 23, 2022

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang
Graph Drawing Graph Learning Real World Graph Structural Embeddings Structural Knowledge Structure Encoder Domain Information

November 22, 2022

A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Roberto Perera, Vinamra Agrawal
Neural Network Machine Learning Transfer Learning Graph Drawing Surface Crack Brittle Fracture Fracture Mechanic

November 19, 2022

Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye
Reinforcement Learning Graph Neural Network Graph Drawing Node Injection Go Game Structural Attack Advantage Actor Critic

November 17, 2022

Spatial Graph Convolution Neural Networks for Water Distribution Systems
Inaam Ashraf, Luca Hermes, André Artelt, Barbara Hammer
Graph Drawing Graph Convolution Message Passing Multi Hop Sparse Signal Water Distribution Network

November 16, 2022

Graph Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman, Santiago Segarra
Machine Learning Convolutional Neural Network Graph Drawing Signal Processing Graph Filter Signal Representation Filter Bank

November 3, 2022

Isotropic Gaussian Processes on Finite Spaces of Graphs
Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause
Graph Drawing Gaussian Process Spherical Gaussian Undirected Graph Computation Kernel Gaussian Process Implicit Surface

October 27, 2022

October 25, 2022

FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Yulin Zhu, Liang Tong, Gaolei Li, Xiapu Luo, Kai Zhou
Graph Neural Network Graph Drawing Robust Version Node Classification Graph Purification

October 24, 2022

Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, Bing Han
Graph Drawing Recommender System Heterogeneous Graph Recommendation Performance Heterogeneous Information Network Session Based

October 22, 2022

ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations
Zhiying Xu, Jiafan Xu, Hongding Peng, Wei Wang, Xiaoliang Wang, Haoran Wan, Haipeng Dai, Yixu Xu, Hao Cheng, Kun Wang, Guihai Chen
Deep Learning Graph Drawing Graph Optimization Brick Wall Deep Learning Compiler Level Loop Alt Text Operator Level Optimization

October 20, 2022

Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data
Alexander Kovalenko, Vitaliy Pozdnyakov, Ilya Makarov
Neural Network Graph Neural Network Graph Drawing Fault Detection Adjacency Matrix Graph Node Graph Level

October 19, 2022