Understanding spreading and evolution in complex networks

Nowadays, the increasing availability of Big Data, describe our desires, opinions, sentiments, purchases, relationships and social connections, provide access to a huge source of information on an unprecedented scale. In the last decades Social Network Analysis, SNA, has received increasing attention from several, different fields of research. Such popularity was certainly due to the flexibility offered by graph representation: a powerful tool that allows reducing many phenomena to a common analytical framework whose basic bricks are nodes and their relations. Especially, the analysis of diffusive phenomena that unfold on top of complex networks is a task able to attract growing interests from multiple fields of research. Understanding the mechanism behind the global spread of an epidemic or information is fundamental for applications in a diversity of areas such as epidemiology or viral marketing. This thesis aims to understand spreading and evolution phenomena in complex networks. We developed two libraries framework: DyNetX, a package designed to model evolving graph topologies and a simulation framework, called NDlib, aimed to model, simulate and study diffusion phenomena that unfold over complex networks. This framework can be fruitfully used by different user segments, from developers to students as well as non-technicians. The purpose of this simulation framework is to empirically compare the effects of diffusion processes according to different diffusion models over several network topologies within different contexts. Covered models include classic and network epidemic models, threshold models and opinion dynamics models; the repertoire of models is extensible. NDlib is the first library that, leveraging DyNetX, allows the implementation of diffusion models explicitly designed to work on top of evolving network topologies. NDlib is about being released on SoBigData.eu. We also investigated connected problems, including the early discovery of successful innovations, and the diminishing return effect in diffusion processes, leveraging on large, real datasets from diverse domains, such as retail and music.