Visualization for Streaming Networks

Sampling from Large Scale Social Networks is a hot topic in recent research. In telecommunications services, there are many networks with millions of nodes and billions of edges. They are complex and difficult to analyze. Sampling, together with vizualization techniques, are required for exploratory data analysis and event detection. Until now, to visualize and analyze the massive network data we would rely on aggregation of communities, k-Core decompositions and matrix feature representations, among others. In social network visualization and analysis the goal is to get more information from the data with the least alienation possible from the actors of the network. Our contribution is to treat the data like a continuous data stream and represent it by sampling the full network. We also propose group visualization and analysis of influential actors in the network by using a Top-K representation of the network data stream.