Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Small Worlds: The Dynamics of Networks between Order and Randomness
Small Worlds: The Dynamics of Networks between Order and Randomness
Big-bang simulation for embedding network distances in Euclidean space
IEEE/ACM Transactions on Networking (TON)
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences
Brownian Agents and Active Particles: Collective Dynamics in the Natural and Social Sciences
The Influence of Customer Churn and Acquisition on Value Dynamics of Social Neighbourhoods
WSKS '09 Proceedings of the 2nd World Summit on the Knowledge Society: Visioning and Engineering the Knowledge Society. A Web Science Perspective
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The dynamic of complex social networks is nowadays one of the research areas of growing importance. The knowledge about the temporal changes of the network topology and characteristics is crucial in networked communication systems in which accurate predictions are important. In this paper a physics-inspired method to track the changes within complex social network is proposed. This method is based on the dynamic molecular modelling technique used in physics for simulation of large sets of interacting particles. The data for the conducted research was derived from e-mail communication within big company (Wroclaw University of Technology). From this information the social network of employees was extracted. The created social network was utilized to evaluate the methodology of social network dynamics modelling proposed by authors.