In this paper, we propose a method to incrementally compute PageRank for a large graph that is evolving. Our approach is quite general, and can be used to incrementally compute (on evolving graphs) any metric that satisfies the first order Markov property.
—This paper presents a generalized version of the linear threshold model for simulating multiple cascades on a network while allowing nodes to switch between them. The proposed model is shown to be a rapidly mixing Markov chain and the corresponding steady state distribution is used to estimate highly likely states of the cascades' spread in the network.… (More)
— Advanced communication technologies enable strangers to work together on the same tasks or projects in virtual environments. Understanding the formation of task-oriented groups is an important first step to study the dynamics of team collaboration. In this paper, we investigated group combat activities in Sony's EverQuest II game to identify the role of… (More)
Interpersonal interaction plays an important role in organizational dynamics, and understanding these interaction networks is key for any organization, since they can be tapped to facilitate various organizational processes. The principal roadblock to studying organizational networks, however, has been the difficulty in collecting data about them. The… (More)
PageRank is a popular ranking metric for large graphs such as theWorld Wide Web. Current research techniques for improving computational efficiency of PageRank have focussed on improving the I/O cost, convergence and parallelizing the computation process. In this paper, we propose a divide and conquer strategy for efficient computation of PageRank. The… (More)