Recent developments in Web 2.0 and Cyberinfrastructure technologies create massive computer mediated networks, where the nodes might be people as well as “non-human agents” such as documents, datasets, analytic tools, and concepts. And these networks become more and more “multidimensional”. Search exploits that links. Search becomes personal, collaborative, social. Network models are capable to aggregate heterogeneous information, graph-based methods provide clear intuition and elegant mathematic to mine such models. The course will provide review of modern graph-based methods, including methods of stochastic physics and clustering approaches needed to analyse the structure of complex networks exhibiting high clustering (such as in networks of friendships between individuals). We will present applications of these methods to mining of large volumes of heterogeneous information, and we will demonstrate how to make these methods aware of dimensions of networks where people are involved, including social, semantics, and activity management dimensions.