TY - GEN
T1 - The Research on Approximating the Real Network Degree Distribution Level Based on DCSBM
AU - Qi, Tianyu
AU - Zhang, Hongwei
AU - Zhan, Yufeng
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2022 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2022
Y1 - 2022
N2 - Many things in the real world can be simplified as a complex system composed of nodes and the relationships between nodes like a graph. But in real life, the actual graph topology that we can get is usually limited. The traditional stochastic block model (SBM) can build graph networks of different sizes. Since the SBM cannot simulate real network well in degree distribution level, this paper aims to study a degree-corrected stochastic block model called DCSBM. We construct the DCSBM in two ways, the stochastic sequence and genetic algorithm constraint. Based on the DCSBM, the phase transition, which shows the theoretical upper limit of the model's performance, was derived by the belief propagation (BP) algorithm. And we use different graph embedding methods, including NetMF, ProNE and BP algorithm, to make some evaluations. We find the DCSBM approximate real graphs well and the phase transition we infer is correct.
AB - Many things in the real world can be simplified as a complex system composed of nodes and the relationships between nodes like a graph. But in real life, the actual graph topology that we can get is usually limited. The traditional stochastic block model (SBM) can build graph networks of different sizes. Since the SBM cannot simulate real network well in degree distribution level, this paper aims to study a degree-corrected stochastic block model called DCSBM. We construct the DCSBM in two ways, the stochastic sequence and genetic algorithm constraint. Based on the DCSBM, the phase transition, which shows the theoretical upper limit of the model's performance, was derived by the belief propagation (BP) algorithm. And we use different graph embedding methods, including NetMF, ProNE and BP algorithm, to make some evaluations. We find the DCSBM approximate real graphs well and the phase transition we infer is correct.
KW - Belief propagation algorithm
KW - Degree-corrected stochastic block model
KW - Random graph models
UR - http://www.scopus.com/inward/record.url?scp=85140470235&partnerID=8YFLogxK
U2 - 10.23919/CCC55666.2022.9902866
DO - 10.23919/CCC55666.2022.9902866
M3 - Conference contribution
AN - SCOPUS:85140470235
T3 - Chinese Control Conference, CCC
SP - 7124
EP - 7129
BT - Proceedings of the 41st Chinese Control Conference, CCC 2022
A2 - Li, Zhijun
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 41st Chinese Control Conference, CCC 2022
Y2 - 25 July 2022 through 27 July 2022
ER -