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CLC number: TP3-05

On-line Access: 2021-11-15

Received: 2020-11-08

Revision Accepted: 2020-12-08

Crosschecked: 2021-01-14

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Citations:  Bibtex RefMan EndNote GB/T7714


Mincheng Wu


Shibo He


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.11 P.1458-1462


Quantifying multiple social relationships based on a multiplex stochastic block model

Author(s):  Mincheng Wu, Zhen Li, Cunqi Shao, Shibo He

Affiliation(s):  College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   s18he@zju.edu.cn

Key Words:  Social network, Multiplex network, Stochastic block model

Mincheng Wu, Zhen Li, Cunqi Shao, Shibo He. Quantifying multiple social relationships based on a multiplex stochastic block model[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1458-1462.

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author="Mincheng Wu, Zhen Li, Cunqi Shao, Shibo He",
journal="Frontiers of Information Technology & Electronic Engineering",
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%0 Journal Article
%T Quantifying multiple social relationships based on a multiplex stochastic block model
%A Mincheng Wu
%A Zhen Li
%A Cunqi Shao
%A Shibo He
%J Frontiers of Information Technology & Electronic Engineering
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%N 11
%P 1458-1462
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000617

T1 - Quantifying multiple social relationships based on a multiplex stochastic block model
A1 - Mincheng Wu
A1 - Zhen Li
A1 - Cunqi Shao
A1 - Shibo He
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 11
SP - 1458
EP - 1462
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000617

Online social networks have attracted great attention recently, because they make it easy to build social connections for people all over the world. However, the observed structure of an online social network is always the aggregation of multiple social relationships. Thus, it is of great importance for real-world networks to reconstruct the full network structure using limited observations. The multiplex stochastic block model is introduced to describe multiple social ties, where different layers correspond to different attributes (e.g., age and gender of users in a social network). In this letter, we aim to improve the model precision using maximum likelihood estimation, where the precision is defined by the cross entropy of parameters between the data and model. Within this framework, the layers and partitions of nodes in a multiplex network are determined by natural node annotations, and the aggregate of the multiplex network is available. Because the original multiplex network has a high degree of freedom, we add an independent functional layer to cover it, and theoretically provide the optimal block number of the added layer. Empirical results verify the effectiveness of the proposed method using four measures, i.e., error of link probability, cross entropy, area under the receiver operating characteristic curve, and Bayes factor.




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