Mobile telecom operators collect and store Call Detail Records (CDRs) in real-time, which detail the communication among subscribers. Call graphs can be induced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale and generated on real-time, the underlying social networks are inherently complex and, thus, difficult to analyze. Conventional data analysis performed by telecom operators is slow, done by request and implies heavy costs in data warehouses. In face of these challenges, real-time streaming analysis becomes an ever increasing need to mobile operators, since it enables them to quickly detect important network events and improve their marketing strategies. Sampling, together with visualization techniques, are required for online exploratory data analysis and event detection in such networks. In this chapter, we report the burgeoning body of research in network sampling, streaming analysis and streaming visualization of social networks and the solutions proposed so far.

The Portuguese governmental network comprising all the 776 ministers and junior ministers who were part of the 19 governments between the year 1976 and 2013 is presented and analyzed. The data contain information on connections concerning business and other types of organizations and, to our knowledge, there is no such extensive research in previous literature. Upon the presentation of the data, a social network analysis considering the temporal dimension is performed at three levels of granularity: network-level, subnetwork-level (political groups) and node-level. A discussion based on the results is presented. We conclude that although it fits two of the four preconditions of a small-world model, the Portuguese governmental network is not a small-world network, although presenting an evolution pointing toward becoming one. Also, we use a resilience test to study the evolution of the robustness of the Portuguese governmental network, pinpointing the moment when a set of members became structurally important.