Visual Analytics of Large-scale Evolving Networks
Data streams arriving from multiple data sources such as sensors, logs, and social media exhibit structural patterns, and can be modeled as time-evolving networks. With the rapid growth in Internet of Things (IoT’s) as well as the availability of large-scale data from social media, sensors and smart phones, there is great interest in structuring real world observations from these sources as evolving networks. The size and complexity of these graphs are however growing to span millions of nodes and billions of edges, and hence present several challenges in terms of processing, analyzing, and visualizing this data. Time-evolving features of large scale graphs are being studied within our research group in various applications, such as disaster management, spread of communicative diseases, link prediction and event-emergence via social network analysis. This presentation will introduce the challenges associated with evolving networks, concepts and techniques relating to storing, preprocessing, analyzing and visualizing these graph data sets, and some of our proposed solutions.