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

机译：了解和建模在线社交网络的复杂动态：可扩展的概念方法

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摘要

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 macrolevel 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, stochastic 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.
机译：在线社交网络的爆炸性增长引起了人类交流中大量的定性和定量变化，这些变化源于个人之间以及社交网络的个人与技术对象之间的直接和间接在线交互。在在线生态系统中，自组织的社区出现并发展，而行为，规范，趋势，信任和集体活动模式则表现为源自相互关联的个体之间的微观互动的宏观属性。对在线（和离线）社会动力过程的研究需要一种方法来捕获其进化性质以及与外部环境的相互作用。一个相关的方法框架是复杂自适应系统的框架，由于强大的交互，适应和学习，网络拓扑结构和节点状态共同发展。社会网络的特征是复杂，随机和非平衡动力学，因此，它们的研究和建模要求采用探索性，零碎和混合的方法，将复杂性，网络理论，动力学系统，定量社会学和统计物理学等领域的概念融合在一起。在本文中，我们将上述学科的方法整合为可扩展的概念方法，以期提供适用于在线和离线社交网络中发生的动态现象的研究和建模的方法和技术建议。

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