Scalable Analysis for Large Social Networks: the data-aware mean-field approach

TL;DR

This paper introduces a scalable, data-aware mean-field model for analyzing large social networks, effectively integrating network and social parameters to overcome computational limitations and better understand social dynamics.

Contribution

It presents a novel mean-field approach that incorporates social attributes, enabling scalable analysis of large social networks while preserving key social and network parameters.

Findings

The model overcomes computational scalability issues.

Large social networks evolve through both network and social factors.

The approach effectively predicts links in large social networks.

Abstract

Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters.