Network Science attracts the attention of a large number of researchers from across various disciplines, mainly due to its applicability in modelling the structure and dynamics of a wide variety of large-scale complex networks, ranging from genetic pathways and ecological networks to the Internet, WWW, peer-to-peer networks, blogs and online social networks, etc. Moreover, Network science has compelling applications in the field of (say) computer communication networks, electric power grid networks, transportation networks, social networks, and biological networks. In the context of the digital-world, the communication and social networks have contributed immensely in generating Big Data pools. Data (say) logged from the Internet, p2p network, online social networks, scientific citations, protein interaction takes naturally the form of networks, often with a temporal/dynamic dimension. When this big data meets the recent advances of network science, decision makers gain an unprecedented insight from the emergent properties, constraints, phase transitions, and so on, that were previously hidden. Recently, data analytics techniques are used to learn the dynamics of massively large complex networks (e.g., link prediction, community formation) generated from large data. This has a substantial impact on the design of efficient, social aware communication systems. Side by side, to design efficient large-scale communication networks of the future, it is imperative to understand the interdependency between the underlying physical network and the information networks that it intends to support. A deeper understanding of such interdependency can only be achieved by closer interaction between network scientists, communication network designers, and social and behavioral scientists.

The goal of this workshop is to a provide a forum where this diverse group of researchers can come together and exchange ideas that will lead to deeper insights into the design of robust, efficient and complex communication networks of the future.