Social Network Analysis has become a core aspect of analyzing networks today. As statistics blended with computer science gave rise to data mining in machine learning, so is the social network analysis, which finds its roots from sociology and graphs in mathematics. In the past decades, researchers in sociology and social sciences used the data from surveys and employed graph theoretical concepts to study the patterns in the underlying networks. Nowadays, with the growth of technology following Moore’s Law, we have an incredible amount of information generating per day. Most of which is a result of an interplay between individuals, entities, sensors, genes, neurons, documents, etc., or their combinations. With the emerging line of networks such as IoT, Web 2.0, Industry 4.0, smart cities and so on, the data growth is expected to be more aggressive. Analyzing and mining such rapidly generating evolving forms of networks is a real challenge. There are quite a number of research works concentrating on analytics for static and aggregated networks. Nevertheless, as the data is growing faster than computational power, those methods suffer from a number of shortcomings including constraints of space, computation and stale results. While focusing on the above challenges, this dissertation encapsulates contributions in three major perspectives: Analysis, Sampling, and Mining of streaming networks.