一种面向动态科研网络的社区检测算法

Community Detection Algorithm for Dynamic Academic Network

科研网络是一类动态变化的异构信息网络,科研网络上的社区检测能挖掘出学术主体的所属社区并发现蕴含于科研社区中的洞察。既有的社区检测算法忽略了科研网络的动态特征和科研主体间的特殊关系,未将科研社区内部的紧密程度和社区间的关系纳入社区检测算法中予以优化,对此提出了一种基于动态科研网络表示学习的社区检测算法DANE-CD。首先基于科研网络自编码器学习科研网络中学术主体的表示向量,然后创新性地在表示学习过程中融入了基于模块度和团队断裂带两个维度的聚类优化,最后基于堆栈自编码器构造了动态科研网络表示学习模型,同时完成了对科研网络的社区检测。在DBLP和HEP-TH两个真实科研数据集上进行了实验,实验结果显示算法在准确率、归一化互信息和模块度3个指标上优于既有科研社区检测算法,可以较好地完成动态科研网络下的社区检测任务。

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 existing 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.