Community structure mining in big data social media networks with MapReduce

Social media networks are playing increasingly prominent role in people's daily life. Community structure is one of the salient features of social media network and has been applied to practical applications, such as recommendation system and network marketing. With the rapid expansion of social media size and surge of tremendous amount of information, how to identify the communities in big data scenarios has become a challenge. Based on our previous work and the map equation (an equation from information theory for community mining), we develop a novel distributed community structure mining framework. In the framework, (1) we propose a new link information update method to try to avoid data writing related operations and try to speedup the process. (2) We use the local information from the nodes and their neighbors, instead of the pagerank, to calculate the probability distribution of the nodes. (3) We exclude the network partitioning process from our previous work and try to run the map equation directly on MapReduce. Empirical results on real-world social media networks and artificial networks show that the new framework outperforms our previous work and some well-known algorithms, such as Radetal, FastGN, in accuracy, velocity and scalability.