TY - GEN
T1 - Incorporating structural diversity of neighbors in a diffusion model for social networks
AU - Bao, Qing
AU - CHEUNG, Kwok Wai
AU - ZHANG, Yu
N1 - Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Diffusion is known to be an important process governing the behaviours observed in network environments like social networks, contact networks, etc. For modeling the diffusion process, the Independent Cascade Model (IC Model) is commonly adopted and algorithms have been proposed for recovering the hidden diffusion network based on observed cascades. However, the IC Model assumes the effects of multiple neighbors on a node to be independent and does not consider the structural diversity of nodes' neighbourhood. In this paper, we propose an extension of the IC Model with the community structure of node neighbours incorporated. We derive an expectation maximization (EM) algorithm to infer the model parameters. To evaluate the effectiveness and efficiency of the proposed method, we compared it with the IC model and its variants that do not consider the structural properties. Our empirical results based on the MemeTracker dataset, shows that after incorporating the structural diversity, there is a significant improvement in the modelling accuracy, with reasonable increase in run-time.
AB - Diffusion is known to be an important process governing the behaviours observed in network environments like social networks, contact networks, etc. For modeling the diffusion process, the Independent Cascade Model (IC Model) is commonly adopted and algorithms have been proposed for recovering the hidden diffusion network based on observed cascades. However, the IC Model assumes the effects of multiple neighbors on a node to be independent and does not consider the structural diversity of nodes' neighbourhood. In this paper, we propose an extension of the IC Model with the community structure of node neighbours incorporated. We derive an expectation maximization (EM) algorithm to infer the model parameters. To evaluate the effectiveness and efficiency of the proposed method, we compared it with the IC model and its variants that do not consider the structural properties. Our empirical results based on the MemeTracker dataset, shows that after incorporating the structural diversity, there is a significant improvement in the modelling accuracy, with reasonable increase in run-time.
KW - Diffusion network
KW - Independent Cascade Model
KW - Social networks
KW - Structural diversity
UR - http://www.scopus.com/inward/record.url?scp=84893263087&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2013.61
DO - 10.1109/WI-IAT.2013.61
M3 - Conference proceeding
AN - SCOPUS:84893263087
SN - 9781479929023
T3 - Proceedings- 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
SP - 431
EP - 438
BT - Proceedings- 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
PB - IEEE Computer Society
T2 - 2013 12th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013
Y2 - 17 November 2013 through 20 November 2013
ER -