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The goal of this research project is to analyze the dynamics of social networks using machine learning   techniques to locate maximal cliques and to find clusters for the purpose of identifying a target   demographic. Unsupervised machine learning techniques are designed and implemented in this project to   analyze a dataset from YouTube to discover communities in the social network and find central nodes.   Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known   Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained   from this research could be used for advertising purposes and for building smart recommendation systems.   All algorithms were implemented using Python programming language. The experimental results show that   we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing   clique detection algorithms, the research shown how machine learning algorithms can detect close knit   groups within a larger network.

Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks