Scholarlink

<p>Community detection is essential for analysing the structural organization schemes of complex networks that are encountered in social, biological, and communication systems. Five prominent community detection techniques, namely, the random node head technique (RNHT), the highest-degree node head technique (HDNHT),<!-- Query ID="Q1" Text="The abberiviation "DDC–CD" was present in the title page but missing in the main manuscript. We followed the title page. Please check and confirm whether it is corerct." --> the Louvain community detection technique, the max–min technique, and the newly proposed degree–distance centroid community detection technique, were compared in this study. The DDC–CD approach uniquely combines the node degree and shortest-path distance metrics to select optimal centroids, thereby promoting the formation of balanced, well-separated community structures. Experiments were carried out on four real-world datasets—Facebook, Power-Grid, Wiki-Vote, and Gnutella—using three key evaluation metrics: the average fitness value, modularity, and runtime.<!-- Query ID="Q2" Text="Please confirm if the author names are presented accurately and in the correct sequence (given name, family name). Author Given name: [R. Leela] Last name [Velusamy]." --> The DDC–CD method consistently produced the lowest average fitness values (0.240 for Facebook, 0.340 for Power-Grid, 0.190 for Wiki-Vote, and 0.280 for Gnutella), reflecting minimal intercommunity connectivity. It also achieved higher modularity values for most datasets (0.531 for Facebook, 0.731 for Power-Grid, 0.141 for Wiki-Vote, and 0.227 for Gnutella), indicating the superior structural quality of its identified communities. While the runtime of DDC–CD was marginally higher than those of the RNHT and HDNHT, it remained computationally efficient and offered a more balanced trade-off relative to those of the max–min and Louvain techniques. In summary, the degree–distance centroid community detection technique surpassed the existing methods by generating compact, cohesive, and well-defined communities, demonstrating improved accuracy and stability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Detecting communities in complex networks using a degree–distance centroid-based method (DDC–CD)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

查看全文
  • Srinivas Amedapu,
  • R. Leela Velusamy

摘要

Community detection is essential for analysing the structural organization schemes of complex networks that are encountered in social, biological, and communication systems. Five prominent community detection techniques, namely, the random node head technique (RNHT), the highest-degree node head technique (HDNHT), the Louvain community detection technique, the max–min technique, and the newly proposed degree–distance centroid community detection technique, were compared in this study. The DDC–CD approach uniquely combines the node degree and shortest-path distance metrics to select optimal centroids, thereby promoting the formation of balanced, well-separated community structures. Experiments were carried out on four real-world datasets—Facebook, Power-Grid, Wiki-Vote, and Gnutella—using three key evaluation metrics: the average fitness value, modularity, and runtime. The DDC–CD method consistently produced the lowest average fitness values (0.240 for Facebook, 0.340 for Power-Grid, 0.190 for Wiki-Vote, and 0.280 for Gnutella), reflecting minimal intercommunity connectivity. It also achieved higher modularity values for most datasets (0.531 for Facebook, 0.731 for Power-Grid, 0.141 for Wiki-Vote, and 0.227 for Gnutella), indicating the superior structural quality of its identified communities. While the runtime of DDC–CD was marginally higher than those of the RNHT and HDNHT, it remained computationally efficient and offered a more balanced trade-off relative to those of the max–min and Louvain techniques. In summary, the degree–distance centroid community detection technique surpassed the existing methods by generating compact, cohesive, and well-defined communities, demonstrating improved accuracy and stability.