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• W2949551437 abstract "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." @default.
• W2949551437 created "2019-06-27" @default.
• W2949551437 creator A5033767306 @default.
• W2949551437 creator A5060005215 @default.
• W2949551437 date "2011-09-18" @default.
• W2949551437 modified "2023-09-27" @default.
• W2949551437 title "Benefits of Bias: Towards Better Characterization of Network Sampling" @default.
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• W2949551437 hasPublicationYear "2011" @default.
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