Iterative mining algorithm based on overlapping communities in social networks

Social networks with complex structure and large scale emerged with the development of social network sites. Various network communities gradually form complex structural pattern in the production and living of people. The competitive advantages and community distribution in networks can be obtained through analyzing the structure of community. Therefore the research key point of the current data mining field is how to find out the potential structure of such large-scale social networks. Currently, most of real networks have overlapping communities. All the users can be allocated to different communities according to different allocation rules. But the complex structure of network and mass node information are difficulties for mining large-scale social network communities. Based on the discussion of relevant theories of complex network and mining algorithm, this study summarized and analyzed several algorithms for mining overlapping communities and put forward a high-efficient and effective overlapping community iterative mining algorithms. Moreover, experiments were carried out to verify its effectiveness and high efficiency. This work provides an improvement direction of relevant technologies for researchers who engage in network community data mining. © 2018, North Atlantic University Union. All rights reserved.