207 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 13 DOI: 10.4018/978-1-5225-2814-2.ch013 ABSTRACT Techno-social systems generate data, which are rather diferent, than data, traditionally studied in so- cial network analysis and other felds. In massive social networks agents simultaneously participate in several contexts, in diferent communities. Network models of many real data from techno-social systems refect various dimensionalities and rationales of actor’s actions and interactions. The data are inher- ently multidimensional, where “everything is deeply intertwingled”. The multidimensional nature of Big Data and the emergence of typical network characteristics in Big Data, makes it reasonable to address the challenges of structure detection in network models, including a) development of novel methods for local overlapping clustering with outliers, b) with near linear performance, c) preferably combined with the computation of the structural importance of nodes. In this chapter the spreading connectivity based clustering method is introduced. The viability of the approach and its advantages are demonstrated on the data from the largest European social network VK. Spreading Activation Connectivity Based Approach to Network Clustering Alexander Troussov The Russian Presidential Academy of National Economy and Public Administration, Russia Sergey Maruev The Russian Presidential Academy of National Economy and Public Administration, Russia Sergey Vinogradov The Russian Presidential Academy of National Economy and Public Administration, Russia Mikhail Zhizhin Colorado University of Boulder, USA