  Over the past decade network theory has turned out to be a powerful
methodology to investigate complex systems of various sorts. Through data
analysis, modeling, and simulation quite an unparalleled insight into their
structure, function, and response can be obtained. In human societies
individuals are linked through social interactions, which today are
increasingly mediated electronically by modern Information Communication
Technology thus leaving "footprints" of human behaviour as digital records. For
these datasets the network theory approach is a natural one as we have
demonstrated by analysing the dataset of multi-million user mobile phone
communication-logs. This social network turned out to be modular in structure
showing communities where individuals are connected with stronger ties and
between communities with weaker ties. Also the network topology and the
weighted links for pairs of individuals turned out to be related.These
empirical findings inspired us to take the next step in network theory, by
developing a simple network model based on basic network sociology mechanisms
to get friends in order to catch some salient features of mesoscopic community
and macroscopic topology formation. Our model turned out to produce many
empirically observed features of large-scale social networks. Thus we believe
that the network theory approach combining data analysis with modeling and
simulation could open a new perspective for studying and even predicting
various collective social phenomena such as information spreading, formation of
societal structures, and evolutionary processes in them.
