NATERGM: A Model for Examining the Role of Nodal Attributes in Dynamic Social Media Networks

Shan Jiang, Hsinchun Chen

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics. This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Temporal patterns in social media networks are modeled based on the nodal attributes of individuals and the time information of network ties. Using social media data collected from a knowledge sharing community, empirical tests were conducted to evaluate the performance of the NATERGM on identifying the temporal patterns and predicting the characteristics of the future networks. Results showed that the NATERGM demonstrated an enhanced pattern testing capability and an increased prediction accuracy of network characteristics compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.

Original languageEnglish (US)
Article number7303952
Pages (from-to)729-740
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number3
DOIs
StatePublished - Mar 1 2016

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Keywords

  • Graphs and networks
  • Knowledge sharing
  • Social networking
  • Web mining

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications

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