Understanding and modeling the complex dynamics of the online social networks: a scalable conceptual approach

Abstract

The explosive growth of the online social networks gives rise to extensive qualitative and quantitative changes in human communication stemming from the direct and indirect online interaction among individuals, as well as between individuals and technological objects of the social web. In the online ecosystem self-organised communities emerge and evolve, while behavior, norms, trends, trust and collective activity patterns appear as macro-level properties originating from micro-level interactions among interconnected individuals. The study of online (and offline) social dynamical processes requires an approach capturing their evolutionary nature and their interplay with the external environment. A pertinent methodological framework is that of the Complex Adaptive Systems, whereby the network topology and the states of the nodes co-evolve owing to strong interaction, adaptation and learning. Social networks are characterized by complex, stoch-astic and non-equilibrium dynamics, and therefore their study and modeling call for an exploratory, piecemeal and hybrid approach bringing together concepts from the fields of complexity, network theory, dynamical systems, quantitative sociology and statistical physics. In this paper we consolidate methods from the aforementioned disciplines into a scalable conceptual approach, with a view to providing methodological and technical recommendations applicable to the study and modeling of dynamical phenomena occurring in online and offline social networks.

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