Modeling idiosyncratic properties of collaboration networks revisited

Ergin Elmacioglu, Dongwon Lee

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.

Original languageEnglish (US)
Pages (from-to)195-216
Number of pages22
JournalScientometrics
Volume80
Issue number1
DOIs
StatePublished - Jan 1 2009

Fingerprint

Digital libraries
Law
remedies
Values
time

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

@article{ead396c2ef434ef4ae306264a4993802,
title = "Modeling idiosyncratic properties of collaboration networks revisited",
abstract = "A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.",
author = "Ergin Elmacioglu and Dongwon Lee",
year = "2009",
month = "1",
day = "1",
doi = "10.1007/s11192-007-2047-7",
language = "English (US)",
volume = "80",
pages = "195--216",
journal = "Scientometrics",
issn = "0138-9130",
publisher = "Springer Netherlands",
number = "1",

}

Modeling idiosyncratic properties of collaboration networks revisited. / Elmacioglu, Ergin; Lee, Dongwon.

In: Scientometrics, Vol. 80, No. 1, 01.01.2009, p. 195-216.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modeling idiosyncratic properties of collaboration networks revisited

AU - Elmacioglu, Ergin

AU - Lee, Dongwon

PY - 2009/1/1

Y1 - 2009/1/1

N2 - A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.

AB - A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.

UR - http://www.scopus.com/inward/record.url?scp=67650784411&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67650784411&partnerID=8YFLogxK

U2 - 10.1007/s11192-007-2047-7

DO - 10.1007/s11192-007-2047-7

M3 - Article

AN - SCOPUS:67650784411

VL - 80

SP - 195

EP - 216

JO - Scientometrics

JF - Scientometrics

SN - 0138-9130

IS - 1

ER -