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arXiv:1109.3911cs.SI

Benefits of Bias: Towards Better Characterization of Network Sampling

Arun S. Maiya, Tanya Y. Berger-Wolf

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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.

arXiv comments
9 pages; KDD 2011: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Journal reference
DOI
Subjects
cs.SISocial and Information Networks physics.soc-phPhysics and Society

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  1. A friendly introduction to the Friendship Paradox (2021) 1 comment

    • True. My comment was to highlight how wrong my initial intuition was. Looking around, this article[1] by Arun Maiya and Tanya Berger-Wolf explores this in the context of social networks. Fascinating stuff! [1] https://arxiv.org/pdf/1109.3911.pdf

      romwellView on HNParent context

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