Visualization of sub-network sets by iterative graph sampling from large scale networks

Multi-layer network visualization techniques have been developed so that users can firstly overview the largescale network and then explore the interesting parts of the data. Meanwhile, local features of the networks are often more interesting rather than their overall structures. It often happens with particular kinds of applications such as social networks. We developed a visualization technique for such types of large-scale networks. The technique iteratively applies a graph sampling algorithm to extract small-scale sub-networks from a large-scale network and then visualize the features of the sub-networks as hierarchically arranged icons. User-specified sub-networks are then visualized by applying our own graph visualization technique. Using networks generated from Twitter data, we actually visualize small-scale networks using the proposed method.