6 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…
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…
Synthetic Networks That Preserve Edge Connectivity
Lahari Anne, The-Anh Vu-Le, Minhyuk Park +2
Since true communities within real-world networks are rarely known, synthetic networks with planted ground truths are valuable for evaluating the performance of community detection…