A generalised significance test for individual communities in networks
Abstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various asp...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2018-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-018-25560-z |
| _version_ | 1818841583398682624 |
|---|---|
| author | Sadamori Kojaku Naoki Masuda |
| author_facet | Sadamori Kojaku Naoki Masuda |
| author_sort | Sadamori Kojaku |
| collection | DOAJ |
| description | Abstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks. |
| first_indexed | 2024-12-19T04:28:23Z |
| format | Article |
| id | doaj.art-b4ea965928db49e5a29d0ebbea1c97f5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T04:28:23Z |
| publishDate | 2018-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b4ea965928db49e5a29d0ebbea1c97f52022-12-21T20:35:58ZengNature PortfolioScientific Reports2045-23222018-05-018111010.1038/s41598-018-25560-zA generalised significance test for individual communities in networksSadamori Kojaku0Naoki Masuda1CREST, JST, Kawaguchi Center BuildingDepartment of Engineering Mathematics, Merchant Venturers Building, University of BristolAbstract Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.https://doi.org/10.1038/s41598-018-25560-z |
| spellingShingle | Sadamori Kojaku Naoki Masuda A generalised significance test for individual communities in networks Scientific Reports |
| title | A generalised significance test for individual communities in networks |
| title_full | A generalised significance test for individual communities in networks |
| title_fullStr | A generalised significance test for individual communities in networks |
| title_full_unstemmed | A generalised significance test for individual communities in networks |
| title_short | A generalised significance test for individual communities in networks |
| title_sort | generalised significance test for individual communities in networks |
| url | https://doi.org/10.1038/s41598-018-25560-z |
| work_keys_str_mv | AT sadamorikojaku ageneralisedsignificancetestforindividualcommunitiesinnetworks AT naokimasuda ageneralisedsignificancetestforindividualcommunitiesinnetworks AT sadamorikojaku generalisedsignificancetestforindividualcommunitiesinnetworks AT naokimasuda generalisedsignificancetestforindividualcommunitiesinnetworks |