2011/09/18 by Arun S. Maiya, Maiya, Arun S., Tanya Y. Berger-Wolf +1 · 1 citation
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #H.2.8 #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1109.3911
9 pages; KDD 2011: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
arxiv created 2011/09/18 · arxiv updated 2011/09/20
From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in fact, beneficial for many applications, as they "push" the sampling process towards inclusion of desired properties. Finally, we describe how these sampling biases can be exploited in several, real-world applications including disease outbreak detection and market research.