Coarse-Grained Topology Estimation via Graph Sampling

Many online networks are measured and studied via sampling techniques, which typically collect a relatively small fraction of nodes and their associated edges. Past work in this area has primarily focused on obtaining a representative sample of nodes and on efficient estimation of local graph properties (such as node degree distribution or any node attribute) based on that sample. However, less is known about estimating the global topology of the underlying graph. In this paper, we show how to efficiently estimate the coarse-grained topology of a graph from a probability sample of nodes. In particular, we consider that nodes are partitioned into categories (e.g., countries or work/study places in OSNs), which naturally defines a weighted category graph. We are interested in estimating (i) the size of categories and (ii) the probability that nodes from two different categories are connected. For each of the above, we develop a family of estimators for design-based inference under un...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Maciej Kurant, Minas Gjoka, Yan Wang, Zack W. Almquist, Carter T. Butts, Athina Markopoulou
• Keywords: Category Graphs, CORR 2011, Education, Friendship Graph, Weighted Category Graph