Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately...
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The research of network community structure based on a large number of complex network datasets is becoming popular in recent years. For the limit of existing computing capabilities and other conditions, such a large ...
详细信息
The research of network community structure based on a large number of complex network datasets is becoming popular in recent years. For the limit of existing computing capabilities and other conditions, such a large network data processing is becoming one of the hardest issues, so sampling algorithm research has become a new hot spot in network data analysis. Based on the needs of network structure research, in this paper, we propose an improved forestfires algorithm, which can not only decrease the scale of network data but also maintain the previous network community structure well. We define two concepts, namely community degree' and center of community' in the algorithm. Then the algorithm was applied on five datasets. In order to make it convenient for the comparison between our sampling algorithm and the other six sampling algorithms under different parameters, we use network community profile and Kolmogorov-Smirno D statistics to judge the consistency between the sample and the previous graph. Experiment results show that the improved algorithm is better than the other six sampling algorithms under most of the parameters. The efficiency and feasibility of the modified algorithm is also validated. Finally, we give the recommended values of different parameters. Copyright (c) 2014 John Wiley & Sons, Ltd.
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