Chen Yinan, Ye Wenbin, Li Dong
Department of Computer Science and Technology, Shantou University, Shantou 515821, China.
School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
Entropy (Basel). 2023 Dec 3;25(12):1617. doi: 10.3390/e25121617.
To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms.
针对传统谱聚类算法无法获取网络完整结构信息的问题，本文提出了一种基于点互信息（PMI）图核的谱聚类社区检测算法PMIK-SC。该核根据节点间的点互信息构建，然后用作相似度矩阵来重构网络并获得对称归一化拉普拉斯矩阵。最后，通过拉普拉斯矩阵的特征分解和特征向量聚类对网络进行划分。此外，为了在谱聚类过程中确定聚类数量，本文提出了一种快速算法BI-CNE来估计社区数量。对于特定网络，该算法首先重构原始网络，然后运行蒙特卡罗采样通过贝叶斯推理估计社区数量。实验结果表明，该算法在估计社区数量方面的检测速度和准确性优于其他现有算法。在此基础上，谱聚类社区检测算法PMIK-SC与其他社区检测算法和谱聚类算法相比，也具有较高的准确性和稳定性。
