Modeling scientific-citation patterns and other triangle-rich acyclic networks

TL;DR

This paper introduces a model for scientific citation networks that accounts for aging and triangle formation, accurately matching empirical data and revealing insights into how papers' impact diminishes over time.

Contribution

The paper presents a novel model incorporating aging and triangle formation to better simulate the evolution of citation networks, validated against real data.

Findings

Optimal model parameters match empirical network structures.

Impact of papers is inversely proportional to time since publication.

Model effectively captures triangle-rich acyclic network features.

Abstract

We propose a model of the evolution of the networks of scientific citations. The model takes an out-degree distribution (distribution of number of citations) and two parameters as input. The parameters capture the two main ingredients of the model, the aging of the relevance of papers and the formation of triangles when new papers cite old. We compare our model with three network structural quantities of an empirical citation network. We find that an unique point in parameter space optimizing the match between the real and model data for all quantities. The optimal parameter values suggest that the impact of scientific papers, at least in the empirical data set we model is proportional to the inverse of the number of papers since they were published.