7 papers
Dense Subgraph Clustering and a New Cluster Ensemble Method
The-Anh Vu-Le, João Alfredo Cardoso Lamy, Tomás Alessi +5
We propose DSC-Flow-Iter, a new community detection algorithm that is based on iterative extraction of dense subgraphs. Although DSC-Flow-Iter leaves many nodes unclustered, it is…
Using Stochastic Block Models for Community Detection: The issue of edge-connectivity
The-Anh Vu-Le, Minhyuk Park, Ian Chen +2
A relevant, sometimes overlooked, quality criterion for communities in graphs is that they should be well-connected in addition to being edge-dense. Prior work has shown that leadi…
Improved Community Detection using Stochastic Block Models
Minhyuk Park, Daniel Wang Feng, Siya Digra +4
Identifying edge-dense communities that are also well-connected is an important aspect of understanding community structure. Prior work has shown that community detection methods c…
Improved Community Detection using Stochastic Block Models
Minhyuk Park, Daniel Wang Feng, Siya Digra +3
Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block mod…
EC-SBM Synthetic Network Generator
The-Anh Vu-Le, Lahari Anne, George Chacko +1
Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synth…
RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation
Lahari Anne, The-Anh Vu-Le, Minhyuk Park +2
The limited availability of useful ground-truth communities in real-world networks presents a challenge to evaluating and selecting a "best" community detection method for a given…