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Extra resources for 3D Reconstruction of Tomographic Images Applied to Largely Spaced Slices
Many ﬁelds need computing the similarity between objects, such as recommendation system, search engine etc. Simrank is one of the simple and intuitive algorithms. It is rigidly based on the random walk theorem. There are three existing iterative ways to compute simrank, however, all of them have one problem, that is time consuming; moreover, with the rapidly growing data on the Internet, we need a novel parallel method to compute simrank on large scale dataset. Hadoop is one of the popular distributed platforms.
And clearly, the total space cost does not exceed O(n+m). And this signiﬁcant space saving will show great advantage in large graphs. 66GHz PC with 4G memory. Programming language is C/C++. TRS: A New Structure for Shortest Path Query 23 Table 1. TRS structure on artiﬁcial graph Node no. 100 200 300 400 500 600 700 800 900 1000 Edge no. 1 RE no. 15 7 15 26 21 29 54 37 35 78 SE no. 15 31 34 38 57 43 54 71 60 75 The experiments ﬁrst record the time and space cost for constructing TRS for graphs. We generated an artiﬁcial graph, whose number of nodes ranging from 100 to 1000, and the saturations are mostly lower than 1 %.
The limitation step length is determined by the diameter of the graph. When using this method computing simrank, it is important to choose a proper step length. If the length is too short, a random walk track will not cover some nodes, and if the length is too long there are circles in the walking track. The following experiment is supposed to conﬁrm the inﬂuence the sampling time to the accuracy. As comparing the accuracy on a big graph is a diﬃcult job, a small graph is chosen here. 088 4 5 Parallel Simrank Computing on Large Scale Dataset on Mapreduce 37 Then begin random walk on the reverse graph, the walking length is 8, and 3 iterations is enough.