title: Active semi-supervised community detection based on must-link and cannot-link constraints.
abstract: Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods.
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
authors.forename: Jianjun
authors.initials: J
authors.lastname: Cheng
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
authors.forename: Mingwei
authors.initials: M
authors.lastname: Leng
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
authors.forename: Longjie
authors.initials: L
authors.lastname: Li
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
authors.forename: Hanhai
authors.initials: H
authors.lastname: Zhou
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D020407:Internet; D016571:Neural Networks, Computer