Applied Network Science

Table 1 Original statistics of network datasets used prior to sampling

From: Using a Bayesian approach to reconstruct graph statistics after edge sampling

Dataset	N	M	\(k_{\text {max}}\)	\(\rho\)	T	\(\bar{C}\)	\(\bar{T_l}\)
Erdős-Rényi	1000	10,000	35	− 0.002	1373	0.021	0.41
Barabási-Albert	1000	9900	170	− 0.041	6099	0.063	1.85
Hep-Th	5835	13,815	50	0.185	10,624	0.506	2.31
AS Topology	11,174	23,409	2389	− 0.195	19,894	0.296	2.55
MathOverflow	24,759	187,985	2172	− 0.215	1,403,896	0.313	22.4

Shown is the number of nodes N, number of edges M, maximum degree \(k_{\text {max}}\), degree assortativity \(\rho\), number of triangles T, average node clustering coefficient \(\bar{C}\) (Watts and Strogatz 1998), and average number of triangles per link \(\bar{T_l}\)

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