对比多种算法在社交网络中的社区发现效果
Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset
- 基于模块度等指标，比较吕万、吉尔万-纽曼等算法性能
- 发现不同算法在社区划分的紧凑性与分离性上表现各异
- 适合社交网络分析与算法选型参考
在网络安全研究中，社区检测始终是网络科学中的重要课题，已有大量论文和算法致力于揭示网络中的潜在结构。本文对若干主流社区检测算法在来自 Facebook 社交媒体网络的 SNAP Social Circles 数据集上进行对比分析。所用算法包括 Louvain、Girvan-Newman、Spectral Clustering、K-Means Clustering 等。通过模块度（modularity）、归一化切割比（normalized cut-ratio）、轮廓系数（silhouette score）、紧凑性（compactness）和可分性（separability）等多个指标评估算法性能。研究结果揭示了各算法在识别社交网络中有意义社区方面的有效性与局限性，深化了对社区检测方法的理解，并为真实社交网络分析提供了实用指导。
原文摘要 · Abstract (English)
In network research, Community Detection has always been a topic of significant interest in network science, with numerous papers and algorithms proposing to uncover the underlying structures within networks. In this paper, we conduct a comparative analysis of several prominent community detection algorithms applied to the SNAP Social Circles Dataset, derived from the Facebook Social Media network. The algorithms implemented include Louvain, Girvan-Newman, Spectral Clustering, K-Means Clustering, etc. We evaluate the performance of these algorithms based on various metrics such as modularity, normalized cut-ratio, silhouette score, compactness, and separability. Our findings reveal insights into the effectiveness of each algorithm in detecting various meaningful communities within the social network, shedding light on their strength and limitations. This research contributes to the understanding of community detection methods and provides valuable guidance for their application in analyzing real-world social networks.
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