An Estimated Closeness Centrality Ranking Algorithm for Large-Scale Workflow Affiliation Networks

A type of workflow affiliation network is one of the specialized social network types, which represents the associative relation between actors and activities. There are many methods on a workflow affiliation network measuring centralities such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In particular, we are interested in the closeness centrality measurements on a workflow affiliation network discovered from enterprise workflow models, and we know that the time complexity problem is raised according to increasing the size of the workflow affiliation network. This paper proposes an estimated ranking algorithm and analyzes the accuracy and average computation time of the proposed algorithm. As a result, we show that the accuracy improves 47.5%, 29.44% in the sizes of network and the rates of samples, respectively. Also the estimated ranking algorithm`s average computation time improves more than 82.40%, comparison with the original algorithm, when the network size is 2400, sampling rate is 30%.