Name
Abstract | ||
|---|---|---|
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 OPT-IA and HYBRID-IA, 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 OPT-IA and HYBRID-IA 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 LOUVAIN algorithm. The experimental analysis conducted proves that OPT-IA and HYBRID-IA are reliable optimization methods for community detection, outperforming all compared algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/a13060139 | ALGORITHMS |
Keywords | DocType | Volume |
community detection,optimization,modularity optimization,complex networks,metaheuristics,immunological-inspired computation | Journal | 13 |
Issue | Citations | PageRank |
6 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| vincenzo cutello | 1 | 553 | 57.63 |
| Georgia Fargetta | 2 | 0 | 0.34 |
| Mario Pavone | 3 | 212 | 19.41 |
| Rocco A. Scollo | 4 | 0 | 0.34 |