Methods and Applications of Network Sampling

Presenters: Mohammad A. Hasan, Indiana University-Purdue University Indianapolis, USA;  Nesreen K. Ahmed, Purdue University, USA;  Jennifer Neville, Purdue University, USA;  
Tutorial Website:

Network data appears in various domains, including social, communication, and information sciences. Analysis of such data is crucial for making inferences and predictions about these networks, and moreover, for understanding the different processes that drive their evolution. However, a major bottleneck to perform such an analysis is the massive size of real-life networks, which makes modeling and analyzing these networks simply infeasible. Further, many networks, specifically those that belong to social and communication domains, are not visible to the public due to privacy concerns, and other networks, such as the Web, are only accessible via crawling. Therefore, to overcome the above challenges, researchers use network sampling overwhelmingly as a key statistical approach to select a sub-population of interest that can be studied thoroughly.

In this tutorial, we aim to cover a diverse collection of methodologies and applications of network sampling. We will begin with a discussion of the problem setting in terms of objectives (such as, sampling a representative subgraph, sampling graphlets, etc.), population of interest (vertices, edges, motifs), and sampling methodologies (such as Metropolis-Hastings, random walk, and snowball sampling). We will then present a number of applications of these methods, and will outline both the resulting opportunities and possible biases of different methods in each application.

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