2011 BenefitsofBiasTowardsBetterChar

• (Maiya & Berger-Wolf, 2011) ⇒ Arun S. Maiya, and Tanya Y. Berger-Wolf. (2011). “Benefits of Bias: Towards Better Characterization of Network Sampling.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020431

Subject Headings: Network Sampling.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Benefits+of+Bias%3A+Towards+Better+Characterization+of+Network+Sampling
• http://dl.acm.org/citation.cfm?id=2020408.2020431&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; bias; complex networks; crawling; data mining; experimentation; graph mining; link mining; measurement; online sampling; sampling; social network analysis

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 BenefitsofBiasTowardsBetterChar	Tanya Berger-Wolf Arun S. Maiya			Benefits of Bias: Towards Better Characterization of Network Sampling				10.1145/2020408.2020431		2011