Sunday 24th of September 2017
 

Empirical Studies on Methods of Crawling Directed Networks


Junjie Tong, Haihong E, Meina Song and Junde Song

Online Social Network has attracted lots of academies and industries to look into its characteristics, models and applications. There are many methods for crawling or sampling in networks, especially for the undirected networks. We focus on sampling the directed networks and intend to compare the efficiency, the accuracy and the stability between them. We consider the sampled nodes and links as a whole and separated from the original one. We evaluate experiments by deploying the snow ball method, the random walk method, DMHRW and MUSDSG with different sampling ratios on the datasets. The snow ball method and random walk method both have bias towards low outdegree nodes while the snow ball method tends to sample more hub nodes. DMHRW and MUSDSG can sample the network parallel but more complex than the snow ball and the random walk under the same sampling ratio. DMHRW will be the best choice of all while the computation capability and time are sufficient.

Keywords: Sampling Method, Directed Networks, Measurements, Graph Sampling

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ABOUT THE AUTHORS

Junjie Tong
Junjie Tong received his bachelor degree in computer science from China University of Mining and Technology in Beijing, and now is a Ph.D. candidate at the Department of Computer Science and Technology of Beijing University of Posts and Telecommunication. His research interests include CDN and complex networks.

Haihong E
Haihong E is a Ph.D., Lecturer at the Department of Computer Science and Technology of Beijing University of Posts and Telecommunication. Her research interests include service sciences and engineering, service network, and trusted service.

Meina Song
Meina Song is a Ph.D. Associate Professor at the Department of Computer Science and Technology of Beijing University of Posts and Telecommunication. Her research interests include service methodology, service system architecture, service sciences and engineering.

Junde Song
Junde Song is a Ph.D. Professor, Doctoral Advisor at the Department of Computer Science and Technology of Beijing University of Post & Telecom. His research interests include parallel computing, service sciences and engineering.


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