Large graph clustering using DCT-based graph clustering

With the proliferation of the World Wide Web, graph structures have arisen on social network/media sites. Such graphs usually number several million nodes, i.e., they can be characterized as Big Data. Graph clustering is an important analysis tool for other graph related tasks, such as compression, community discovery and recommendation systems, to name a few. We propose a novel extension to a graph clustering algorithm, that attempts to cluster a graph, through the optimization of selected terms of the graph weight/adjacency matrix Discrete Cosine Transform.