Literature DB >> 25615146

Community detection in networks: Structural communities versus ground truth.

Darko Hric1, Richard K Darst1, Santo Fortunato1.   

Abstract

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

Year:  2014        PMID: 25615146     DOI: 10.1103/PhysRevE.90.062805

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  19 in total

1.  Default mode network abnormalities in posttraumatic stress disorder: A novel network-restricted topology approach.

Authors:  Teddy J Akiki; Christopher L Averill; Kristen M Wrocklage; J Cobb Scott; Lynnette A Averill; Brian Schweinsburg; Aaron Alexander-Bloch; Brenda Martini; Steven M Southwick; John H Krystal; Chadi G Abdallah
Journal:  Neuroimage       Date:  2018-05-03       Impact factor: 6.556

2.  A Shadowing Problem in the Detection of Overlapping Communities: Lifting the Resolution Limit through a Cascading Procedure.

Authors:  Jean-Gabriel Young; Antoine Allard; Laurent Hébert-Dufresne; Louis J Dubé
Journal:  PLoS One       Date:  2015-10-13       Impact factor: 3.240

3.  A Comparative Analysis of Community Detection Algorithms on Artificial Networks.

Authors:  Zhao Yang; René Algesheimer; Claudio J Tessone
Journal:  Sci Rep       Date:  2016-08-01       Impact factor: 4.379

4.  Structure and inference in annotated networks.

Authors:  M E J Newman; Aaron Clauset
Journal:  Nat Commun       Date:  2016-06-16       Impact factor: 14.919

5.  SCOUT: simultaneous time segmentation and community detection in dynamic networks.

Authors:  Yuriy Hulovatyy; Tijana Milenković
Journal:  Sci Rep       Date:  2016-11-24       Impact factor: 4.379

6.  Locating Structural Centers: A Density-Based Clustering Method for Community Detection.

Authors:  Xiaofeng Wang; Gongshen Liu; Jianhua Li; Jan P Nees
Journal:  PLoS One       Date:  2017-01-03       Impact factor: 3.240

7.  Detection of communities with Naming Game-based methods.

Authors:  Thais Gobet Uzun; Carlos Henrique Costa Ribeiro
Journal:  PLoS One       Date:  2017-08-10       Impact factor: 3.240

8.  The ground truth about metadata and community detection in networks.

Authors:  Leto Peel; Daniel B Larremore; Aaron Clauset
Journal:  Sci Adv       Date:  2017-05-03       Impact factor: 14.136

9.  Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

Authors:  Lovro Šubelj; Nees Jan van Eck; Ludo Waltman
Journal:  PLoS One       Date:  2016-04-28       Impact factor: 3.240

10.  Codon Bias Patterns of E. coli's Interacting Proteins.

Authors:  Maddalena Dilucca; Giulio Cimini; Andrea Semmoloni; Antonio Deiana; Andrea Giansanti
Journal:  PLoS One       Date:  2015-11-13       Impact factor: 3.240

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