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A Graph Database Supported GA-Based Approach to Social Network Analysis

With the rapid growth of social networks, their analysis plays an increasingly important role in marketing and research. This reveals the need for efficient and effective techniques for doing so. In certain problem domains (e.g. marketing and education), detecting clusters of users with similar characteristics allows for services to be targeted to specific communities. While a number of approaches have been proposed in the literature to this end, they suffer from scalability issues when analysing larger or more densely connected social networks. To cope with this scalability problem, a Genetic Algorithm (GA)-based approach for social network analysis is proposed in this paper, supported by a graph database to store the static relationships between the nodes, thus eliminating the computational cost of repeatedly searching the network. Thus, the novelty of this work is in the combination of GA-based approach with a graph database, which store data more effectively, which allows for global optimisation to take place. Additionally, the main advantage of the proposed approach is that it does not require a priori knowledge regarding the social network when performing the analysis. The GA-based approach is shown to have the best performance regarding the identification of communities, though also requiring higher execution times.