Community Detection and Mining Using Complex Networks Tools in Social Internet of Things

Abstract

in recent time rapid and extraordinary technological advancement is dominated by the social internet of things (SIoT). SIoT connects people together socially and opens doors to people, to share ideas by using this information. Typically, SIoT deals with the massive amount of data and information. This data is used by various online social networks (ONS), i.e. Twitter, LinkedIn and Facebook etc. analyzing and mining of useful extracted information from these social networks is not an easy task. SIoT has a special interest in numerous research fields, i.e. computer sciences and social sciences. The detection and mining of a community reveal how the structure affects the people and their relationships. In order to facilitate the community discovery, a wide range of tools has been developed over years. Each of them differs from other, in respect of features and benefits. Choosing the right tool is somehow a difficult task. In order to overcome this difficulty, our work offers an analysis by dividing them into various categories such as network platform, algorithm complexity, community detection and their execution time. Finally, we discussed various visualization layouts of social networks which are helpful in order to precise the network data.