TY - JOUR
T1 - A Component-Based Diffusion Model with Structural Diversity for Social Networks
AU - Bao, Qing
AU - CHEUNG, Kwok Wai
AU - ZHANG, Yu
AU - LIU, Jiming
N1 - Funding information:
This paper was supported in part by the General Research Fund through the Research Grants Council of Hong Kong Special Administrative Region under Grant HKBU210410, and in part by the Natural Science Foundation of China under Grant 61305071.
PY - 2017/4
Y1 - 2017/4
N2 - Diffusion on social networks refers to the process where opinions are spread via the connected nodes. Given a set of observed information cascades, one can infer the underlying diffusion process for social network analysis. The independent cascade model (IC model) is a widely adopted diffusion model where a node is assumed to be activated independently by any one of its neighbors. In reality, how a node will be activated also depends on how its neighbors are connected and activated. For instance, the opinions from the neighbors of the same social group are often similar and thus redundant. In this paper, we extend the IC model by considering that: 1) the information coming from the connected neighbors are similar and 2) the underlying redundancy can be modeled using a dynamic structural diversity measure of the neighbors. Our proposed model assumes each node to be activated independently by different communities (or components) of its parent nodes, each weighted by its effective size. An expectation maximization algorithm is derived to infer the model parameters. We compare the performance of the proposed model with the basic IC model and its variants using both synthetic data sets and a real-world data set containing news stories and Web blogs. Our empirical results show that incorporating the community structure of neighbors and the structural diversity measure into the diffusion model significantly improves the accuracy of the model, at the expense of only a reasonable increase in run-time.
AB - Diffusion on social networks refers to the process where opinions are spread via the connected nodes. Given a set of observed information cascades, one can infer the underlying diffusion process for social network analysis. The independent cascade model (IC model) is a widely adopted diffusion model where a node is assumed to be activated independently by any one of its neighbors. In reality, how a node will be activated also depends on how its neighbors are connected and activated. For instance, the opinions from the neighbors of the same social group are often similar and thus redundant. In this paper, we extend the IC model by considering that: 1) the information coming from the connected neighbors are similar and 2) the underlying redundancy can be modeled using a dynamic structural diversity measure of the neighbors. Our proposed model assumes each node to be activated independently by different communities (or components) of its parent nodes, each weighted by its effective size. An expectation maximization algorithm is derived to infer the model parameters. We compare the performance of the proposed model with the basic IC model and its variants using both synthetic data sets and a real-world data set containing news stories and Web blogs. Our empirical results show that incorporating the community structure of neighbors and the structural diversity measure into the diffusion model significantly improves the accuracy of the model, at the expense of only a reasonable increase in run-time.
KW - Diffusion networks
KW - Independent cascade model
KW - social networks
KW - Structural diversity
UR - http://www.scopus.com/inward/record.url?scp=84961595157&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2016.2537366
DO - 10.1109/TCYB.2016.2537366
M3 - Journal article
C2 - 28113881
AN - SCOPUS:84961595157
SN - 2168-2267
VL - 47
SP - 1078
EP - 1089
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 4
M1 - 7437433
ER -