Statistical monitoring for change detection of interactions between nodes in networks: With a case study in financial interactions network

Abstract

In the real world, monitoring the number of transactions between nodes in many networks such as transportation, sales, financial communications, etc. is very important, from which network stakeholders can enjoy significant benefits, as well. The present paper attempts to show a significant reduction in the performance of the statistical methods in detecting network anomalies resulting from losing information due to disregarding the weights of edges in the case of modeling and monitoring weighted networks through using binary models. This paper focuses on and applies normal distribution in a real non social network because the statistical distribution of edges in most weighted networks is normal. The performance of the statistical model in the form of a case study, monitoring electronic components exchange network in a repair company, is described. Using simulation, the ability of the model in detecting network anomalies is compared with the binary model.

Keywords:

• Change detection
• Likelihood Ratio Test (LRT)
• social network
• attributed network
• regression