Complex network structures, where real-world systems are modelled, contain important information that can be uncovered. Various studies have been carried out, and many methods have been proposed recently to discover such information by using different network analysis techniques. The discovery of meaningful modules in networks is one of these significant works. In this study, a new hybrid method, which is called uniSFLA, is proposed to determine statistically significant modules within the network. Another significant aspect of this study is to use various objective functions as fitness criteria and compare the results obtained from the tests with each other. The aim is to test the success of various objective functions used to investigate network modules and those defined according to different properties in graphs. The proposed algorithm was tested on real-world networks, and the test results were compared with those of other algorithms from published literature. Considering the experimental results, the method suggested in this work produced significant success in terms of both best and average values. Moreover, the accuracy and quality tests of the conformity values obtained for each objective function were performed with four different cluster evaluation criteria. Finally, in addition to the successful results for the uniSFLA algorithm, the comparative test results of appropriate network modules, obtained using modularity and significance functions, were evaluated by means of various tables and graphs. (C) 2017 Elsevier B.V. All rights reserved.