ELPMeans通过拉普拉斯中心性与聚类结合，无需预设社区数即可高效识别复杂网络结构。
Community Detection by ELPMeans: An Unsupervised Approach That Uses Laplacian Centrality and Clustering
- 融合拉普拉斯中心性与层次聚类、K均值算法，自动识别核心节点
- 在真实网络上准确率更高，时间复杂度显著降低
- 适用于非凸形状社区，无需预先知道社区数量
社交网络规模激增使社区检测愈发复杂。本文提出一种新方法ELPMeans，通过结合拉普拉斯中心性、层次聚类与K均值算法，实现全网社区检测。该方法利用拉普拉斯中心性与最小距离度量识别核心节点，借助K均值学习加速收敛至最终社区结构。值得注意的是，ELPMeans为无监督方法，实现简单，有效解决核心节点随机初始化及社区数K难确定等常见问题。实验表明，该算法在真实网络上显著提升准确率并降低时间复杂度，优于近期主流方法。其适用范围广，可处理非凸形状社区，且无需事先知晓社区数量。
原文摘要 · Abstract (English)
Community detection in network analysis has become more intricate due to the recent hike in social networks (Cai et al., 2024). This paper suggests a new approach named ELPMeans that strives to address this challenge. For community detection in the whole network, ELPMeans combines Laplacian, Hierarchical Clustering as well as K-means algorithms. Our technique employs Laplacian centrality and minimum distance metrics for central node identification while k-means learning is used for efficient convergence to final community structure. Remarkably, ELPMeans is an unsupervised method which is not only simple to implement but also effectively tackles common problems such as random initialization of central nodes, or finding of number of communities (K). Experimental results show that our algorithm improves accuracy and reduces time complexity considerably outperforming recent approaches on real world networks. Moreover, our approach has a wide applicability range in various community detection tasks even with nonconvex shapes and no prior knowledge about the number of communities present.
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