Locality and attachedness-based temporal social network growth dynamics analysis: A case study of evolving nanotechnology scientific collaboration networks

Haizheng Zhang, Baojun Qiu, Kristinka Ivanova, C. Lee Giles, Henry C. Foley, John Yen

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fastevolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.

Original languageEnglish (US)
Pages (from-to)964-977
Number of pages14
JournalJournal of the American Society for Information Science and Technology
Volume61
Issue number5
DOIs
StatePublished - May 1 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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