%0 Conference Paper %1 Maiya:2011:BBT:2020408.2020431 %A Maiya, Arun S. %A Berger-Wolf, Tanya Y. %B Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining %C New York, NY, USA %D 2011 %I ACM %K bias network sampling %P 105--113 %R 10.1145/2020408.2020431 %T Benefits of bias: towards better characterization of network sampling %U http://doi.acm.org/10.1145/2020408.2020431 %X 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. %@ 978-1-4503-0813-7

@inproceedings{Maiya:2011:BBT:2020408.2020431, 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.}, acmid = {2020431}, added-at = {2012-04-04T09:16:22.000+0200}, address = {New York, NY, USA}, author = {Maiya, Arun S. and Berger-Wolf, Tanya Y.}, biburl = {https://www.bibsonomy.org/bibtex/2ba21a23b1085f260378c6e93458d1d36/emrahcem}, booktitle = {Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining}, description = {Benefits of bias}, doi = {10.1145/2020408.2020431}, interhash = {01b014331a4f1b1555f4abe945a78a17}, intrahash = {ba21a23b1085f260378c6e93458d1d36}, isbn = {978-1-4503-0813-7}, keywords = {bias network sampling}, location = {San Diego, California, USA}, numpages = {9}, pages = {105--113}, publisher = {ACM}, series = {KDD '11}, timestamp = {2012-04-04T23:45:56.000+0200}, title = {Benefits of bias: towards better characterization of network sampling}, url = {http://doi.acm.org/10.1145/2020408.2020431}, year = 2011 }