A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing meta heuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any intense interest to reflect the domain knowledge. Moreover, all the published reviews did not make any direct effort to link heuristic and metaheuristic based community detection approaches, rather, they simply state them separately. The review introduced in this paper attempts to address this issue. Mainly, we review the main heuristic and metaheuristic based community detection algorithms. Then, we introduce two new taxonomies for community detection algorithms: hybrid metaheuristic and hyper heuristic that can serve as common grounds for designing a collection of new and more effective MCD algorithms. To this end, we introduce four new systematic frameworks integrating both heuristic and metaheuristic algorithms, illustrating the possible issues that would fuel the desire for researchers to direct their future interest towards developing more effective community detection instances from the context of these frameworks.

Automated summary

Current metaheuristic based community detection algorithms tend to reflect a traditional language, lacking depth in reflecting domain knowledge. This paper introduces a new review approach attempting to link heuristic and metaheuristic based community detection methods, proposing two new taxonomies and introducing four new systematic frameworks that integrate both heuristic and metaheuristic algorithms to provide new ideas for designing more effective community detection algorithms.