TY - JOUR
T1 - Multi-Topic Tracking Model for dynamic social network
AU - Li, Yuhua
AU - Liu, Changzheng
AU - Zhao, Ming
AU - Li, Ruixuan
AU - Xiao, Hailing
AU - Wang, Kai
AU - Zhang, Jun
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China under Grants 61572221 , 61300222 , 61173170 , 61433006 and U1401258 , State Key Laboratory of Software Engineering under grants SKLSE2012-09-11 , and Innovation Fund of Huazhong University of Science and Technology under Grants 2015TS069 and 2015TS071 , and Youth Foundation of National Computer network Emergency Response technical Team/Coordination Center of China under Grant 2013QN-19 , Science and Technology Support Program of Hubei Province under grant 2014BCH270 and 2015AAA013 , and Science and Technology Program of Guangdong Province under grant 2014B010111007 .
Publisher Copyright:
© 2016 Elsevier B.V. All rights reserved.
PY - 2016/7/15
Y1 - 2016/7/15
N2 - The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.
AB - The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.
KW - Dynamic social network
KW - Influence phenomenon
KW - Multi-Topic Tracking Model
KW - Selection phenomenon
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U2 - 10.1016/j.physa.2016.02.038
DO - 10.1016/j.physa.2016.02.038
M3 - Article
AN - SCOPUS:84960912858
SN - 0378-4371
VL - 454
SP - 51
EP - 65
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
ER -