Relational Network Sciences and Complexity

Relational Network Sciences and Complexity is a research area that studies a synergistic convergence of the traditional social science study of social networks and the recently-emerged network science. Network Science studies the fundamental principles underlying the structure and the dynamic behaviors that are common among a wide range of large-scale networks (e.g., collaborative networks, power grid networks, networks of the Web, social networks, and communication networks). 

Traditional social network study, on the other hand, focuses on the multi-dimensional relationships between people in a small group. Relational Network Science combines these two disciplines to enable innovations to better predict the complex system behaviors of networks through a richer representation of the multi-dimensional relationship between nodes (or communities) in a network. Furthermore, it will provide insights about the relationship between the micro-level models (e.g., the probabilistic model about link creations) and the macro-level phenomena (e.g., network percolation) of networks. 

These network models and analyses can discover communities and patterns hidden in a network, predict the evolution of networks over time and space, identify factors that influence the dynamics of networks, and enhance the robustness of networks. Techniques in Relational Network Sciences and Complexity are drawn from traditional social network studies, probabilistic and statistical models, mathematical models of complex systems, stochastic mechanics, artificial intelligence, modeling and simulation, information sciences, and operation research.