Community Detection Algorithm for Dynamic Academic Network

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.