Triangle Sampling Based Estimation of Average Degree of Network Using Different Estimator

Abstract

Graph are use to represent complex relationship which includes online social network, pandemic spread network and other real world networks. In recent time, graph sampling is used for study of different parameters of network, such includes many sampling algorithms like Random node, Random edge sampling, Rank degree, etc. which are used for collection of subset of network, but efficient estimation methods are not discussed much for parameter estimation. This paper presents a comparison of different estimators comprises Simple random sampling, Ratio and Regression estimator to estimate the average degree of network. To collect sample technique of triangles are collected using seed nodes. A comparative procedure is used to obtain the lower and upper limit of confidence intervals with the help of multiple samples. Ogive based simulation is also used for single value computation of both limits of confidence interval(CI). The results obtained from simulation, show that Regression estimator is more efficient than the Ratio and Simple random sampling based estimators.