In this paper, for the first time, we tackle the scalability problem of Influence Maximization (IM) via distributed computing. First, we propose a distributed IM algorithm based on IRIE, one of the most state-of-the-art IM algorithms. Then an incremental updating method is proposed to reduce the overhead of repeated computation. Furthermore, based on some new insights, we redesign our algorithm with a strategy, which we call reservoir, to accumulate increments and delay exchange between machines. Experiments on real-world and synthetic networks show our redesigned algorithm, i.e. dIRIEr (distributed IRIE with Reservoir), reduces communication traffic dramatically and speeds up continuously as more machines are added in. dIRIEr can handle giant networks with hundreds of millions of nodes where centralized algorithms become infeasible. Keywords-distributed algorithm; parallel; influence maximization; social networkDeep insights motivates us to design a messagemerge-and-delay strategy to reduce communication traffic, dramatically improving efficiency of parallel computing.We conduct extensive experiments on large-scale real-world and synthetic networks of different size. Through comparing running time, memory usage and communication overhead, we show that our distributed algorithm scales well with more machines joining computing. Finally, dIRIEr succeeds in handling a graph of 160 million nodes within about 10000 seconds while centralized IRIE failed before it can load the graph into memory. II. PROBLEM SETUP IC ( Independent Cascade ) ModelIC model is the most widely used information diffusion model in influence maximization and IRIE is based on this 978-1-4799-7615-7/14/$31.00 ©2014 IEEE
This paper addresses the issues of rate control and routing for cloud data center networks. Based on the theory of the supply-demand equilibrium, we propose a fixed point model for formulating cloud network equilibrium problems in which the equilibrium conditions are given by nonlinear equations. We show that the network equilibrium point is the optimal solution of a nonlinear programming problem by utilizing the tools of the variational inequality and convex optimization. The augmented Lagrangian multiplier algorithm is used to solve the nonlinear programming problem for computing the network equilibrium point. Further consideration is given to the equilibrium problems of a cloud network with multirate multicast sessions. We evaluate our approach on some random networks with unicast and multicast sessions, and the results show the effectiveness of our approach in finding the optimal equilibrium rates. We further evaluate the performance of our approach through cloud data center network simulation under various parameter settings, and the results show that the performance of our algorithm can be tuned and improved by choosing appropriate parameter values.
In this paper, the multi-scale finite element model (FEM) of a composite cable-stayed bridge, Guanhe Bridge, was established based on the Arlequin method firstly. Then a two-step multi-scale FE model updating method was proposed. Furthermore, based on structural health monitoring (SHM) system of Guanhe Bridge, support vector regression (SVR) method was employed to analyse the uncertainty quantification and transmission. It was shown that the errors between the calculated frequencies from the updated multi-scale FEM and the measured frequencies from SHM were less than 3%. In the procedure of inverse uncertainty propagation, the coincidence indexes of the structural parameters were larger than 65%. The deviations between the optimal values of the updated parameters and the corresponding statistical mean values were very small (<5%). Finally, the analysis results indicate that the distributions of the parameters agree well with the assumed normal distribution.
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