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. Empirical results showed that the NATERGM demonstrated an enhanced pattern testing capability compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.
Original language | English (US) |
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Title of host publication | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1458-1459 |
Number of pages | 2 |
ISBN (Electronic) | 9781509020195 |
DOIs | |
State | Published - Jun 22 2016 |
Event | 32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland Duration: May 16 2016 → May 20 2016 |
Other
Other | 32nd IEEE International Conference on Data Engineering, ICDE 2016 |
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Country | Finland |
City | Helsinki |
Period | 5/16/16 → 5/20/16 |
Keywords
- dynamic networks
- knowledge sharing
- social media
ASJC Scopus subject areas
- Artificial Intelligence
- Computational Theory and Mathematics
- Computer Graphics and Computer-Aided Design
- Computer Networks and Communications
- Information Systems
- Information Systems and Management