Community Detection
227 papers with code • 14 benchmarks • 12 datasets
Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.
Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Benchmarks
These leaderboards are used to track progress in Community Detection
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Libraries
Use these libraries to find Community Detection models and implementations
Pubmed
Cora
SNAP
Placenta
Latest papers with no code
Systematic review of image segmentation using complex networks
This review presents various image segmentation methods using complex networks.
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature
This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks.
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks
Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace.
A Framework for Exploring Federated Community Detection
Federated Learning is machine learning in the context of a network of clients whilst maintaining data residency and/or privacy constraints.
Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection
(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised community detection of clustered nodes is attributed to their capacity to encode both the connectivity and feature information spaces of graphs.
Uncovering communities of pipelines in the task-fMRI analytical space
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline.
Community Detection in High-Dimensional Graph Ensembles
Detecting communities in high-dimensional graphs can be achieved by applying random matrix theory where the adjacency matrix of the graph is modeled by a Stochastic Block Model (SBM).
Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models
It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities.
Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment
After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI.
Understanding Opinions Towards Climate Change on Social Media
In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media.
