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Bias Correction in Clustering Coefficient Estimation

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  • Fri, 03/02/2018 - 11:00am - 12:00pm




Bias Correction in Clustering Coefficient Estimation

Computer Science Colloquium Series
Roohollah Etemadi

Date:  Friday, March 2nd, 2018
Time: 11:00 am
Location: Odette, 110

Abstract: This talk will cover our recent study on Clustering Coefficient estimation in real-world networks. Clustering coefficient (C) is an important structural property to understand the complex structure of networks. Calculating C is computationally an expensive task. Thereby, sampling based methods have attracted substantial research for estimating C, and the closely related metric, the number of triangles. A widely used estimator for C is biased. We quantify the bias using Taylor expansion and find that the bias can be determined by the number of shared wedges and triangles in the sample. Based on the understanding of the bias, we give a new estimator that corrects the bias. The results are derived analytically and verified extensively in 56 networks ranging in different size and structure.

Bio: Roohollah Etemadi is a Ph.D. Candidate at the School of Computer Science at the University of Windsor. He has been working as a research assistant under the supervision of Dr. Jianguo Lu, and Dr. Yung H. Tsin since 2015. His recent research interests are in the areas of big data analysis, scalable algorithms, sampling techniques, and graph mining. The results of his research have been published in top-tier conferences, CIKM and Big Data. He has also received several scholarships and awards including Ontario Graduate Scholarship (OGS), SIGIR Student Travel Grant, and IEEE Big Data Student Travel Award based on his academic and research excellence.

 



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