Estimation of Average Degree of Social Network Using Clique, Shortest Path and Cluster Sampling to monitor Network Reliability.

In recent past, Online Social Networks (OSN) has emerged as a platform for sharing information, thoughts, and activities. In the real-world network, method of considering the appropriate samples is most frequently used for network analysis. Graph sampling is a procedure used for computing unknown parameters. Many sampling algorithms exist in literature such as Random node, Random edge sampling, Rank degree, etc. can be used for estimation. This paper presents a comparison of clique based procedure (CBP) and shortest path based procedure (SPP) to estimate the average degree of a vertex in a social network using an overlapping cluster sampling. 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 limits of CI. The results, obtained from simulation, show that clique based sampling algorithm (CBP) is more efficient than the shortest path based sampling algorithm (SPP). The estimated confidence intervals can be used for monitoring the reliability of a social network in terms of control over average network degree. [ABSTRACT FROM AUTHOR]