Community Detection via Local Learning Based on Generalized Metric With Neighboring Regularization

Gao, Guangliang; Wu, Zhang; Zhang, Lu; Cao, Jie; Qi, Xinzhe

doi:10.1109/tsmc.2020.3003019

Cited by 5 publications

(2 citation statements)

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“…Extensive research [46,47] proposes quality metrics such as modularity Q [48] and normalized cut [49] based on such a principle, and achieves community detection by maximizing or minimizing the value of the given quality metric. However, it is not true that the optimization of quality metrics always performs satisfactorily, e.g., the resolution limit of modularity [50], the unbalanced scale of communities [23], the free-rider effect [24]. Although the value of the corresponding quality metric is optimal, the natural community structure of a network is not clearly revealed.…”

Section: Two-level Constraintsmentioning

confidence: 99%

“…Secondly, it is a consensus [5,6,[11][12][13][14][15][16] that we wish the nodes belonging to the same community are with dense edges and homogeneous features while the nodes falling into different communities are not. However, it is unwise to focus on nothing but the optimal value of the objective function to achieve the above goal, since it may lead the communities we find suffer from overload, unbalance [23] or free-rider effect [24]. Thirdly, scalability, ignored by many researchers, is an important factor to consider.…”

Section: Introductionmentioning

confidence: 99%

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Triangle-oriented Community Detection considering Node Features and Network Topology

Gao¹,

Liang²,

Yuan³

et al. 2022

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The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a trianglebased quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both nonoverlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

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