Computer Science ›› 2022, Vol. 49 ›› Issue (1): 89-94.doi: 10.11896/jsjkx.210100023

Special Issue: Big Data & Data Scinece

• Database & Big Data & Data Science • Previous Articles     Next Articles

Community Detection Algorithm for Dynamic Academic Network

PU Shi, ZHAO Wei-dong   

  1. School of Software,Fudan University,Shanghai 200433,China
    Shanghai Key Laboratory of Data Science,Shanghai 200433,China
  • Received:2021-01-04 Revised:2021-04-19 Online:2022-01-15 Published:2022-01-18
  • About author:PU Shi,born in 1997,postgraduate,is a member of China Computer Federation.His main research interests include data mining and recommendation systems.
    ZHAO Wei-dong,born in 1971,Ph.D,associate professor.His main research interests include intelligent data analysis and decision support systems.
  • Supported by:
    National Nature Science Foundation of China( 61671157) and Major Project of Philosophy and Social Science Research,Ministry of Education of China(19JZD010).

Abstract: Academic network is a kind of dynamic heterogeneous information network.Community detection on the academic network can dig out the communities of academic subjects and discover the insights contained in the community structure.The exis-ting community detection algorithms ignore the dynamics of the academic network and the special relationship between academic subjects and do not optimize the closeness of the academic community and the relationship between academic communities.This paper proposes a community detection algorithm called DANE-CD based on dynamic academic network representation learning.Firstly,an autoencoder is adopted to represent the academic subject in the academic network.Secondly,the clustering optimization based on modularity and team faultlines is innovatively integrated into the representation learning process.Finally,a dynamic academic network representation model is constructed based on the stacked autoencoder,together with the completion of community detection in the dynamic academic network.Extensive experiments on two real-world academic datasets(DBLP and HEP-TH) demonstrate that DANE-CD is superior to the baseline methods and can detect the academic communities effectively.

Key words: Academic network, Clustering optimization, Community detection, Dynamic network, Heterogeneous network

CLC Number: 

  • TP391
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