A Group-Based Yule Model for Bipartite Author-Paper Networks

TL;DR

This paper introduces a new group-based Yule model for bipartite author-paper networks, capturing collaboration dynamics and publication patterns, validated through simulation on real-world data.

Contribution

It proposes a novel group-based Yule model incorporating collaboration and success-breeds-success dynamics, providing a better understanding of author-paper network structures.

Findings

Model effectively mimics real-world author-paper network behavior

Simulation results align with empirical data from complex network publications

Demonstrates the importance of group organization in publication patterns

Abstract

This paper presents a novel model for author-paper networks, which is based on the assumption that authors are organized into groups and that, for each research topic, the number of papers published by a group is based on a success-breeds-success model. Collaboration between groups is modeled as random invitations from a group to an outside member. To analyze the model, a number of different metrics that can be obtained in author-paper networks were extracted. A simulation example shows that this model can effectively mimic the behavior of a real-world author-paper network, extracted from a collection of 900 journal papers in the field of complex networks.