A Synthetic Visual Analysis System for Time-Varying Network

In recent years, with the rapid development of computer science and information technology, the world is filled and propelled of time-varying and multivariate data. The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for the time-varying network is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: A triangular based algorithm for time-varying behavior presentation and an improved brush PCP aims to assist the visual analysis task in time-varying network pattern detection. A synthetic visual analysis system is designed and implemented on the basis of these two novel methods. Our system is capable of visually analyzing vast amounts of network data. To better describe and demonstrate the usefulness and performance of our system, we utilize the ChinaVis2015 Challenge dataset as a case study.