Optimization Algorithms for Detection of Social Interactions
Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/13/6/139 |
| _version_ | 1797565574348275712 |
|---|---|
| author | Vincenzo Cutello Georgia Fargetta Mario Pavone Rocco A. Scollo |
| author_facet | Vincenzo Cutello Georgia Fargetta Mario Pavone Rocco A. Scollo |
| author_sort | Vincenzo Cutello |
| collection | DOAJ |
| description | Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span>, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span> has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization <span style="font-variant: small-caps;">Louvain</span> algorithm. The experimental analysis conducted proves that <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span> are reliable optimization methods for community detection, outperforming all compared algorithms. |
| first_indexed | 2024-03-10T19:15:05Z |
| format | Article |
| id | doaj.art-7947078ca3454c39a7e5c947a7c92bb8 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T19:15:05Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-7947078ca3454c39a7e5c947a7c92bb82023-11-20T03:30:03ZengMDPI AGAlgorithms1999-48932020-06-0113613910.3390/a13060139Optimization Algorithms for Detection of Social InteractionsVincenzo Cutello0Georgia Fargetta1Mario Pavone2Rocco A. Scollo3Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyDepartment of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyDepartment of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyDepartment of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyCommunity detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span>, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span> has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization <span style="font-variant: small-caps;">Louvain</span> algorithm. The experimental analysis conducted proves that <span style="font-variant: small-caps;">Opt-IA</span> and <span style="font-variant: small-caps;">Hybrid-IA</span> are reliable optimization methods for community detection, outperforming all compared algorithms.https://www.mdpi.com/1999-4893/13/6/139community detectionoptimizationmodularity optimizationcomplex networksmetaheuristicsimmunological-inspired computation |
| spellingShingle | Vincenzo Cutello Georgia Fargetta Mario Pavone Rocco A. Scollo Optimization Algorithms for Detection of Social Interactions Algorithms community detection optimization modularity optimization complex networks metaheuristics immunological-inspired computation |
| title | Optimization Algorithms for Detection of Social Interactions |
| title_full | Optimization Algorithms for Detection of Social Interactions |
| title_fullStr | Optimization Algorithms for Detection of Social Interactions |
| title_full_unstemmed | Optimization Algorithms for Detection of Social Interactions |
| title_short | Optimization Algorithms for Detection of Social Interactions |
| title_sort | optimization algorithms for detection of social interactions |
| topic | community detection optimization modularity optimization complex networks metaheuristics immunological-inspired computation |
| url | https://www.mdpi.com/1999-4893/13/6/139 |
| work_keys_str_mv | AT vincenzocutello optimizationalgorithmsfordetectionofsocialinteractions AT georgiafargetta optimizationalgorithmsfordetectionofsocialinteractions AT mariopavone optimizationalgorithmsfordetectionofsocialinteractions AT roccoascollo optimizationalgorithmsfordetectionofsocialinteractions |