Applying social network analysis to Social Media data supports better assessment of cyber-security threats by analyzing underground Social Media activities, dynamics between cyber-criminals, and topologies of dark networks. However, Social Media data are big and state of the art algorithms for social network analysis metrics require >=O(n + m) space and run in >=O(nm) time - some in O(n^2) or O(n^3) - with n = number of nodes, m = number of edges. Therefore, real-time analysis of Social Media activities to mitigate cyber-security threats with sophisticated social network metrics is not possible. To tackle this problem, we apply ideas of composability to big data and algorithms for social network analysis metrics. A network of humans, organizations, etc. is modeled with a graph G = (N, E) by aggregation of observed interactions E between targeted entities N. Because of the algorithmic complexity, composing network analysis metrics by analyzing sub-networks G1, G2, etc. can result in tremendous gain in calculation time.