Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Estimating and sampling graphs with multidimensional random walks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Understanding Graph Sampling Algorithms for Social Network Analysis
ICDCSW '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops
Measurement of online social networks
Measurement of online social networks
Learning influence in complex social networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Nowadays, Online Social Networks (OSNs) have become dramatically popular and the study of social graphs attracts the interests of a large number of researchers. One critical challenge is the huge size of the social graph, which makes the graph analyzing or even the data crawling incredibly time consuming, and sometimes impossible to be completed. Thus, graph sampling algorithms have been introduced to obtain a smaller subgraph which reflects the properties of the original graph well. Breadth-First Sampling (BFS) is widely used in graph sampling, but it is biased towards high-degree vertices during the process of sampling. Besides, Metropolis-Hasting Random Walk (MHRW), which is proposed to get unbiased samples of the social graph, requires the graph to be well connected. In this paper, we propose a vertex sampling algorithm, so-called Albatross Sampling (AS), which introduces random jump strategy into MHRW during the sampling process. The embedded random jump makes the sampling procedure more flexible and avoids being trapped in some locally well connected part. According to our evaluation, we find that no matter using tightly or loosely connected graphs, AS performs significantly better than MHRW and BFS. On the one hand, AS estimates the degree distribution with much lower Normalized Mean Square Error (NMSE) by consuming the same resource budget. On the other hand, to get an acceptable estimation of the degree distribution, AS requires much less resource budget.