EvoCut : A new Generalization of Albert-Barab\'asi Model for Evolution of Complex Networks

TL;DR

This paper introduces EvoCut, a novel generalization of the Albert-Barabási model, to better capture the dynamic evolution of social networks and their scale-free properties.

Contribution

It proposes a new model that extends the preferential attachment mechanism to more accurately reflect real-world network evolution.

Findings

EvoCut produces networks with realistic degree distributions.

The model captures dynamic node and edge changes over time.

Implications for understanding social network growth are discussed.

Abstract

With the evolution of social networks, the network structure shows dynamic nature in which nodes and edges appear as well as disappear for various reasons. The role of a node in the network is presented as the number of interactions it has with the other nodes. For this purpose a network is modeled as a graph where nodes represent network members and edges represent a relationship among them. Several models for evolution of social networks has been proposed till date, most widely accepted being the Barab\'asi-Albert \cite{Network science} model that is based on \emph{preferential attachment} of nodes according to the degree distribution. This model leads to generation of graphs that are called \emph{Scale Free} and the degree distribution of such graphs follow the \emph{power law}. Several generalizations of this model has also been proposed. In this paper we present a new generalization of the model and attempt to bring out its implications in real life.