一种基于高阶组织的学习者潜在重叠社区检测算法

学习者网络拓扑结构稀疏，且传统的社区检测算法无法为惰性/冷启动学习者检测其潜在的社区。针对该类问题，提出了一种基于高阶组织的学习者潜在重叠社区检测算法(POCDL)。POCDL算法是一种局部图聚类算法，首先利用社交化在线课程平台中的好友关系、同学关系和师生关系解决学习者网络数据稀疏问题；然后挖掘学习者网络中的高阶组织并重构学习者网络；最后，根据学习者的度中心性选取初始种子集，根据社区归属度和社区亲密度进行局部社区检测。在人工网络和学者网真实网络数据集上的实验结果表明：POCDL算法能够较好地为惰性/冷启动学习者检测社区；对其他类型的复杂网络也具有一定的普适性。

A Potential Overlapping Community Detection Algorithm for Learners Based on High-order Organization

Learner network topology is sparse and traditional community detection algorithms cannot detect their potential communities for inert/cold start learners. To address this type of problems, a potential overlapping community detection algorithm (POCDL) for learners based on higher-order organizations is proposed. The POCDL algorithm is a local graph clustering algorithm that firstly uses the friend, classmate and teacher-student relationships in socialized online course platforms to solve the sparse learner network data problem, and then mines the higher-order organizations in the learner network and reconstructs the learner network; finally, the initial seed set is selected based on the degree centrality of learners, and local community detection based on community belongingness and community closeness. Experimental results on artificial networks and SCHOLAT dataset show that the POCDL algorithm can detect communities better for inert/cold-start learners, and also has some generalizability to other types of complex networks.