community detection is a task of fundamental importance in social network analysis. community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. there exist a variety of methods for community detection based on diffe...

Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in ac...

design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. this paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. first, an efficient complex mixed-integer linear programming (milp) model by considering some real-world assumptions is developed for the inte...

Blockmodeling linked networks aims to simultaneously cluster two or more sets of units into clusters based on a network where ties are possible both between from the same set as well different sets. While this has already been developed for generalized and k-means blockmodeling, our approach is well-known stochastic blockmodeling technique, utilizing mixture model. Estimation performed using CE...

Network motifs have been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks. Nonetheless, many networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic networks. The building blocks in these networks thus may also have stochastic properties. In this article...

In this paper, a network-based stochastic information propagation model is developed. The flow modeled by probabilistic differential equation system. numerical solution of these equations leads to the expected number informed nodes as function time and reveals relationship between degrees their reception time. validity justified Monte Carlo network simulation through analysis in scale-free Erdo...