Dense Graph

Dense graphs, characterized by a high number of edges relative to nodes, are a focus of intense research due to their prevalence in real-world networks and the computational challenges they pose. Current research emphasizes developing efficient algorithms for tasks like subgraph discovery, graph generation, and graph neural network (GNN) training on dense graphs, often employing techniques such as graphons, message-passing frameworks, and attention mechanisms to improve scalability and accuracy. These advancements are crucial for addressing problems in diverse fields, including social network analysis, biological networks, and recommendation systems, where dense graph structures are common.

Papers

November 6, 2022

A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
Luana Ruiz, Ningyuan Huang, Soledad Villar
Graph Neural Network Community Detection Sparse Graph Random Graph Model Dense Graph Spectral Analysis Graph Spectrum

August 5, 2022

An Efficient Person Clustering Algorithm for Open Checkout-free Groceries
Junde Wu, Yu Zhang, Rao Fu, Yuanpei Liu, Jing Gao
Practical Algorithm Graph Convolutional Network Nearest Neighbor Dense Graph Open Checkout Free Grocery Efficient Expert

June 11, 2022

Greedy Relaxations of the Sparsest Permutation Algorithm
Wai-Yin Lam, Bryan Andrews, Joseph Ramsey
Practical Algorithm Directed Acyclic Graph Machine Permutation Dense Graph

May 27, 2022

What Dense Graph Do You Need for Self-Attention?
Yuxin Wang, Chu-Tak Lee, Qipeng Guo, Zhangyue Yin, Yunhua Zhou, Xuanjing Huang, Xipeng Qiu
Self Attention Sparse Graph Sparse Transformer Dense Graph Hyper Transformer

April 20, 2022

Effects of Graph Convolutions in Multi-layer Networks
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Mixed Effect Graph Convolution Dense Graph Multilayer Network Multi Layer Network

February 17, 2022

Listing Maximal k-Plexes in Large Real-World Graphs
Zhengren Wang, Yi Zhou, Mingyu Xiao, Bakhadyr Khoussainov
Community Detection Large Graph Dense Graph

January 28, 2022

Neural Approximation of Graph Topological Features
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, Chao Chen
Graph Representation Learning Persistent Homology Dense Graph Neural Network Approximation Graph Topology Persistence Diagram

January 5, 2022

Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning
Itay Levinas, Yoram Louzoun
Subgraph Structure Graph Based Learning Dense Graph

January 1, 2022

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Enyan Dai, Wei Jin, Hui Liu, Suhang Wang
Graph Neural Network Sparse Label Dense Graph Noisy Graph Robust Graph Neural Network

December 2, 2021

ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee
Graph Neural Network Gene Level GNN Dense Graph Built in Interpretability Subgraph Sampling Self Explainable Graph Neural Network