Dynamic egocentric models for citation networks

Duy Q. Vu, Arthur U. Asuncion, David Russell Hunter, Padhraic Smyth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

The analysis of the formation and evolution of networks over time is of fundamental importance to social science, biology, and many other fields. While longitudinal network data sets are increasingly being recorded at the granularity of individual time-stamped events, most studies only focus on collapsed cross-sectional snapshots of the network. In this paper, we introduce a dynamic egocentric framework that models continuous-time network data using multivariate counting processes. For inference, an efficient partial likelihood approach is used, allowing our methods to scale to large networks. We apply our techniques to various citation networks and demon- strate the predictive power and interpretability of the learned statistical models.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages857-864
Number of pages8
Publication statusPublished - Oct 7 2011
Event28th International Conference on Machine Learning, ICML 2011 - Bellevue, WA, United States
Duration: Jun 28 2011Jul 2 2011

Publication series

NameProceedings of the 28th International Conference on Machine Learning, ICML 2011

Other

Other28th International Conference on Machine Learning, ICML 2011
CountryUnited States
CityBellevue, WA
Period6/28/117/2/11

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Human-Computer Interaction
  • Education

Cite this

Vu, D. Q., Asuncion, A. U., Hunter, D. R., & Smyth, P. (2011). Dynamic egocentric models for citation networks. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011 (pp. 857-864). (Proceedings of the 28th International Conference on Machine Learning, ICML 2011).