Imperial College London

Professor Christopher Hankin Director, Institute for Security Science and Technology

Faculty of EngineeringInstitute for Security Science & Technology

Co-Director of Institute for Security Science & Technology



+44 (0)20 7594 7619c.hankin Website




Ms Denise McGurk +44 (0)20 7594 8864




Sherfield BuildingSouth Kensington Campus






BibTex format

author = {Simmie, D and Vigliotti, MG and Hankin, C},
doi = {comnet/cnu024},
journal = {Journal of Complex Networks},
pages = {495--517},
title = {Ranking twitter influence by combining network centrality and influence observables in an evolutionary model},
url = {},
volume = {2},
year = {2014}

RIS format (EndNote, RefMan)

AB - © The authors 2014. Influential agents in networks play a pivotal role in information diffusion. Influence may rise or fall quickly over time and thus capturing this evolution of influence is of benefit to a varied number of application domains such as digital marketing, counter-terrorism or policing. In this paper, we investigate the influence of users in programming communities on Twitter. We propose a new model for capturing both time-invariant influence and also temporal influence. The unified model is a combination of network topological methods and observation of influence-relevant events in the network. We provide an application of Hidden Markov Models (HMM) for capturing this effect on the network. There are many possible combinations of influence factors, hence we required a ground-truth for model configuration. We performed a primary survey of our population users to elicit their views on influential users. The survey allowed us to validate the results of our classifier. We introduce a novel reward-based transformation to the Viterbi path of the observed sequences, which provides an overall ranking for users. Our results show an improvement in ranking accuracy over using solely topology-based methods for the particular area of interest we sampled. Utilizing the evolutionary aspect of the HMM, we attempt to predict future states using current evidence. Our prediction algorithm significantly outperforms a collection of naive models, especially in the short term (1-3 weeks).
AU - Simmie,D
AU - Vigliotti,MG
AU - Hankin,C
DO - comnet/cnu024
EP - 517
PY - 2014///
SN - 2051-1310
SP - 495
TI - Ranking twitter influence by combining network centrality and influence observables in an evolutionary model
T2 - Journal of Complex Networks
UR -
VL - 2
ER -