# 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.

## Key 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.