![](bg6.png)
our algorithm, as well as those from other compara-
tive methods. Our algorithm outperforms the other al-
gorithms for all networks, while ABC algorithm ob-
tains the same results for Karate and Dolphins net-
works. This demonstrates the effectiveness of our
BSODCS in real-world network analysis.
Table 1: Modularity results for all datasets.
Methods Football Karate Dolphins Books
PSO 0.5630 0.3690 - 0.4700
ACO 0.6031 0.4165 0.5628 0.5262
BA 0.5960 0.3920 - 0.4790
ABC 0.6009 0.4198 0.5285 0.5116
BSOCD 0.6040 0.4197 0.5140 -
BSODCS 0.6043 0.4198 0.5285 0,5265
If we consider the Zachary’s karate club, which
naturally consists of two communities of equal size,
our BSODCS algorithm splits the network into four
communities as shown in Fig.4, yielding the highest
modularity value of Q=0.4198 given in Table 1.
Figure 4: Karate Club communities using BSODCS.
5 CONCLUSIONS
In this paper, we investigated the applicability of bee
swarm optimization for community detection prob-
lem. The proposed algorithm, BSODCS, uses the
modularity Q as objective function and starts with
an initial reference solution. A search space is cre-
ated from this reference solution, then a group of bees
collaboratively works to maximize the global func-
tion Q. Each bee operates independently within its
neighborhood and communicates its findings through
a dance. To assess the effectiveness of our algo-
rithm, we conducted experiments on four real-world
networks. The results obtained demonstrate the valid-
ity and efficiency of our method for community de-
tection problem. In future work, we aim to extend
our approach to address community detection prob-
lem in dynamic networks, aiming to further enhance
the quality of the obtained results as well as tracking
communities’ evolution.
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