Network Clustering

Network clustering aims to partition nodes in a network into groups based on their connectivity and associated attributes, maximizing intra-cluster similarity and inter-cluster dissimilarity. Current research emphasizes developing robust algorithms, such as those incorporating k-nearest neighbor augmentation and variational inference, to handle diverse network types (graphs, hypergraphs, multiplex graphs) and account for node attributes or contextual information, often within the framework of stochastic block models. These advancements improve clustering accuracy, particularly in sparse networks, and find applications in diverse fields including social network analysis, bioinformatics, and wireless network management through digital twinning. The resulting improved clustering methods offer more accurate insights into complex systems and facilitate better decision-making in various applications.

Papers

August 10, 2024

A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation
Yiran Li, Gongyao Guo, Jieming Shi, Renchi Yang, Shiqi Shen, Qing Li, Jun Luo
Graph Clustering Attributed Network Node Clustering Multiplex Graph Network Clustering

April 22, 2024

Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning
Zifan Zhang, Mingzhe Chen, Zhaohui Yang, Yuchen Liu
Digital Twin Wireless Network Horizontal Federated Learning 3D Joint Network Clustering

November 27, 2023

Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks
Zhongyuan Lyu, Ting Li, Dong Xia
Block Model Poisson Noise Optimal Clustering Mixture Weight Network Clustering Multi Layer Network

November 26, 2023

Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting
Yasuko Kawahata
Visual Perspective Complex Valued Opinion Dynamic Human Opinion Consensus Formation Substance Use Disorder Network Clustering

January 12, 2023

Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes
Xuezhen Li, Can M. Le
Variational Inference Dense Network Variational Loss Sparse Target Network Clustering

December 20, 2021

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
Guillaume Braun, Hemant Tyagi, Christophe Biernacki
Network Analysis Optimality Guarantee Well Clustered Graph Iterative Clustering Network Clustering Contextual Stochastic Block Model

Network Clustering

Papers

A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation

Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning

Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks

Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting

Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes

An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees