Investigating the Influence of Inverse Preferential Attachment on Network Development.

Cynthia S Q Siew,Michael S Vitevitch

doi:10.3390/e22091029

Cynthia S Q Siew, Michael S Vitevitch

Open Access

https://doi.org/10.3390/e22091029

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Journal: Entropy	Publication Date: Sep 15, 2020
Citations: 11	License type: CC BY 4.0

Affiliation: National University of Singapore, University of Kansas

Abstract

Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure.

Highlights

Many complex systems, such as the Internet, brain networks, and social networks, can be classified as networks—collections of entities connected to each other in a web-like fashion—permitting the application of network analysis to study these systems
Preferential attachment models of network growth, where new nodes that are added to the network tend to connect to existing nodes with many links, have been prominent in the literature covering network growth and evolution, because such models describe a generic mechanism that provides an elegant account of the emergence of scale-free complex networks [2,3,4,5]
PATT refers to networks generated by preferential attachment and iPATT refers to networks generated by inverse preferential attachment

Summary

Introduction

Many complex systems, such as the Internet, brain networks, and social networks, can be classified as networks—collections of entities connected to each other in a web-like fashion—permitting the application of network analysis to study these systems (see [1] for a review). We conducted a series of network simulations to examine the properties of networks grown via a different mechanism, which we refer to as inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Our present approach of simulating network growth via inverse preferential attachment was directly motivated by recent research examining the development of language networks constructed from phonological similarity among words. In these language networks, nodes represent words, while edges are placed between words that share similar sounds [6].

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