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Coarse-Grained Topology Estimation via Graph Sampling

By Maciej Kurant, Minas Gjoka, Yan Wang, Zack W. Almquist, Carter T. Butts and Athina Markopoulou

Abstract

In many online networks, nodes are partitioned into categories (e.g., countries or universities in OSNs), which naturally defines a weighted category graph i.e., a coarse-grained version of the underlying network. In this paper, we show how to efficiently estimate the category graph from a probability sample of nodes. We prove consistency of our estimators and evaluate their efficiency via simulation. We also apply our methodology to a sample of Facebook users to obtain a number of category graphs, such as the college friendship graph and the country friendship graph. We share and visualize the resulting data at www.geosocialmap.com

Topics: Measurement, Algorithms Keywords Online Social Networks, Coarse-Grained Topology, Estimators, Induced Subgraph Sampling, Star sampling, Facebook
Year: 2012
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.514
Provided by: CiteSeerX
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