  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.
