A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
The investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimiza...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018-01-01
|
| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aac06f |
| _version_ | 1797750681661079552 |
|---|---|
| author | Alessandro Muscoloni Carlo Vittorio Cannistraci |
| author_facet | Alessandro Muscoloni Carlo Vittorio Cannistraci |
| author_sort | Alessandro Muscoloni |
| collection | DOAJ |
| description | The investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex networks, which is the community organization. The geometrical-preferential-attachment (GPA) model was recently developed in order to confer to the PSO also a soft community structure, which is obtained by forcing different angular regions of the hyperbolic disk to have a variable level of attractiveness. However, the number and size of the communities cannot be explicitly controlled in the GPA, which is a clear limitation for real applications. Here, we introduce the nonuniform PSO (nPSO) model. Differently from GPA, the nPSO generates synthetic networks in the hyperbolic space where heterogeneous angular node attractiveness is forced by sampling the angular coordinates from a tailored nonuniform probability distribution (for instance a mixture of Gaussians). The nPSO differs from GPA in other three aspects: it allows one to explicitly fix the number and size of communities; it allows one to tune their mixing property by means of the network temperature; it is efficient to generate networks with high clustering. Several tests on the detectability of the community structure in nPSO synthetic networks and wide investigations on their structural properties confirm that the nPSO is a valid and efficient model to generate realistic complex networks with communities. |
| first_indexed | 2024-03-12T16:37:23Z |
| format | Article |
| id | doaj.art-55120f0c4e6b4e0689ef74166771c09d |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:37:23Z |
| publishDate | 2018-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-55120f0c4e6b4e0689ef74166771c09d2023-08-08T14:49:31ZengIOP PublishingNew Journal of Physics1367-26302018-01-0120505200210.1088/1367-2630/aac06fA nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communitiesAlessandro Muscoloni0https://orcid.org/0000-0002-9238-3357Carlo Vittorio Cannistraci1https://orcid.org/0000-0003-0100-8410Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität Dresden , Tatzberg 47/49, D-01307, Dresden, GermanyBiomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität Dresden , Tatzberg 47/49, D-01307, Dresden, Germany; Brain bio-inspired computing (BBC) lab, IRCCS Centro Neurolesi ‘Bonino Pulejo’, Messina, ItalyThe investigation of the hidden metric space behind complex network topologies is a fervid topic in current network science and the hyperbolic space is one of the most studied, because it seems associated to the structural organization of many real complex systems. The popularity-similarity-optimization (PSO) model simulates how random geometric graphs grow in the hyperbolic space, generating realistic networks with clustering, small-worldness, scale-freeness and rich-clubness. However, it misses to reproduce an important feature of real complex networks, which is the community organization. The geometrical-preferential-attachment (GPA) model was recently developed in order to confer to the PSO also a soft community structure, which is obtained by forcing different angular regions of the hyperbolic disk to have a variable level of attractiveness. However, the number and size of the communities cannot be explicitly controlled in the GPA, which is a clear limitation for real applications. Here, we introduce the nonuniform PSO (nPSO) model. Differently from GPA, the nPSO generates synthetic networks in the hyperbolic space where heterogeneous angular node attractiveness is forced by sampling the angular coordinates from a tailored nonuniform probability distribution (for instance a mixture of Gaussians). The nPSO differs from GPA in other three aspects: it allows one to explicitly fix the number and size of communities; it allows one to tune their mixing property by means of the network temperature; it is efficient to generate networks with high clustering. Several tests on the detectability of the community structure in nPSO synthetic networks and wide investigations on their structural properties confirm that the nPSO is a valid and efficient model to generate realistic complex networks with communities.https://doi.org/10.1088/1367-2630/aac06fnetwork modelshyperbolic geometrycommunity structure |
| spellingShingle | Alessandro Muscoloni Carlo Vittorio Cannistraci A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities New Journal of Physics network models hyperbolic geometry community structure |
| title | A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities |
| title_full | A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities |
| title_fullStr | A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities |
| title_full_unstemmed | A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities |
| title_short | A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities |
| title_sort | nonuniform popularity similarity optimization npso model to efficiently generate realistic complex networks with communities |
| topic | network models hyperbolic geometry community structure |
| url | https://doi.org/10.1088/1367-2630/aac06f |
| work_keys_str_mv | AT alessandromuscoloni anonuniformpopularitysimilarityoptimizationnpsomodeltoefficientlygeneraterealisticcomplexnetworkswithcommunities AT carlovittoriocannistraci anonuniformpopularitysimilarityoptimizationnpsomodeltoefficientlygeneraterealisticcomplexnetworkswithcommunities AT alessandromuscoloni nonuniformpopularitysimilarityoptimizationnpsomodeltoefficientlygeneraterealisticcomplexnetworkswithcommunities AT carlovittoriocannistraci nonuniformpopularitysimilarityoptimizationnpsomodeltoefficientlygeneraterealisticcomplexnetworkswithcommunities |