A hybrid multi-objective algorithm based on slime mould algorithm and sine cosine algorithm for overlapping community detection in social networks

In recent years, extensive studies have been carried out in community detection for social network analysis because it plays a crucial role in social network systems in today's world. However, most social networks in the real world have complex overlapping social structures, one of the NP-hard problems. This paper presents a new model for overlapping community detection that uses a multi-objective approach based on a hybrid optimization algorithm. In this model, the Modified Selection Function (MSF) hybrids the algorithms and recovery mechanism, the Slime Mould Algorithm (SMA), the Sine Cosine Algorithm (SCA), and the association strategy. Also, considering that these algorithms have been presented to solve single-objective optimization problems, the Pareto dominance technique has been used to solve multi-objective problems. In addition to overlapping community detection and increasing detection accuracy, the fuzzy clustering technique has been used to select the heads of clusters. Sixteen synthetic and real-world data sets were utilized to assess the suggested model, and the outcomes were contrasted with those of existing optimization techniques. The proposed model has performed better than the other tested algorithms in comparing the tests conducted by us in all 16 data sets, in the comparisons made with the algorithms proposed in other works in 11 data sets out of 14 data. The set has performed better than competitors. As a conclusion, the findings show that this model performs better than other methods.