# The Iterated Local Model for Social Networks

## TL;DR

This paper introduces the Iterated Local Model (ILM), a new generative framework for social networks that combines transitive and anti-transitive mechanisms, capturing key properties of real-world social graphs.

## Contribution

The ILM unifies previous models by integrating both transitive and anti-transitive dynamics based on a binary sequence, advancing the understanding of social network evolution.

## Key findings

- ILM graphs densify and have bounded clustering coefficient
- Graphs exhibit diameter at most 3 and poor spectral expansion
- Analysis of chromatic number, domination, and Hamiltonicity

## Abstract

On-line social networks, such as in Facebook and Twitter, are often studied from the perspective of friendship ties between agents in the network. Adversarial ties, however, also play an important role in the structure and function of social networks, but are often hidden. Underlying generative mechanisms of social networks are predicted by structural balance theory, which postulates that triads of agents, prefer to be transitive, where friends of friends are more likely friends, or anti-transitive, where adversaries of adversaries become friends. The previously proposed Iterated Local Transitivity (ILT) and Iterated Local Anti-Transitivity (ILAT) models incorporated transitivity and anti-transitivity, respectively, as evolutionary mechanisms. These models resulted in graphs with many observable properties of social networks, such as low diameter, high clustering, and densification. We propose a new, generative model, referred to as the Iterated Local Model (ILM) for social networks synthesizing both transitive and anti-transitive triads over time. In ILM, we are given a countably infinite binary sequence as input, and that sequence determines whether we apply a transitive or an anti-transitive step. The resulting model exhibits many properties of complex networks observed in the ILT and ILAT models. In particular, for any input binary sequence, we show that asymptotically the model generates finite graphs that densify, have clustering coefficient bounded away from 0, have diameter at most 3, and exhibit bad spectral expansion. We also give a thorough analysis of the chromatic number, domination number, Hamiltonicity, and isomorphism types of induced subgraphs of ILM graphs.