Evolution Analysis of Call Ego-Networks

Abstract

With the realization of networks in many of the real world domains, research work in network science has gained much attention now-a-days. The real world interaction networks are exploited to gain insights into real world connections. One of the notion is to analyze how these networks grow and evolve. Most of the works rely upon the socio centric networks. The socio centric network comprises of several ego networks. How these ego networks evolve greatly influences the structure of network. In this work, we have analyzed the evolution of ego networks from a massive call network stream by using an extensive list of graph metrics. By doing this, we studied the evolution of structural properties of graph and related them with the real world user behaviors. We also proved the densification power law over the temporal call ego networks. Many of the evolving networks obey the densification power law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them and maintaining the density of graph and decreasing redundancy.

Social network analysis: An overview

“…Analyzing ego networks not only gives insights into the whole network but also can be exploited extensively with memory constraints. The growth pattern of ego networks highly influences the growth of social networks (Tabassum & Gama, 2016a). Epasto et al (2015) argue that it is possible to address important graph mining tasks by analyzing the egonets of a social network and performing independent computations on them.…”

“…The success of these models depends on their ability to incorporate the growth statistics of those networks. Additionally, some studies used the growth statistics of networks to evaluate their sampling techniques (Leskovec & Faloutsos, 2006;Tabassum & Gama, 2016a, 2016b. However, the analysis of networks is also a primary step in developing models in an evolving network mining task.…”

“…Leveraging his observations can help investigate networks to gather information and insights. Tabassum and Gama () employed these measures to compare evolving ego‐networks and their samples, to evaluate their efficiency.…”