دانلود مقاله ISI انگلیسی شماره 79154
ترجمه فارسی عنوان مقاله

یک الگوریتم خوشه بندی سلسله مراتبی بر اساس blockmodeling جدید برای شبکه های اجتماعی وب ☆

عنوان انگلیسی
A new blockmodeling based hierarchical clustering algorithm for web social networks ☆
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79154 2012 8 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Engineering Applications of Artificial Intelligence, Volume 25, Issue 3, April 2012, Pages 640–647

ترجمه کلمات کلیدی
شبکه های اجتماعی وب؛ خوشه بندی سلسله مراتبی؛ Blockmodeling؛ معادل ساختاری؛ بهينه سازي
کلمات کلیدی انگلیسی
Web social networks; Hierarchical clustering; Blockmodeling; Structural equivalence; Optimization
پیش نمایش مقاله
پیش نمایش مقاله  یک الگوریتم خوشه بندی سلسله مراتبی بر اساس blockmodeling جدید برای شبکه های اجتماعی وب ☆

چکیده انگلیسی

Cluster analysis for web social networks becomes an important and challenging problem because of the rapid development of the Internet community like YouTube, Facebook and TravelBlog. To accurately partition web social networks, we propose a hierarchical clustering algorithm called HCUBE based on blockmodeling which is particularly suitable for clustering networks with complex link relations. HCUBE uses structural equivalence to compute the similarity among web pages and reduces a large and incoherent network into a set of smaller comprehensible subnetworks. HCUBE is actually a bottom-up agglomerative hierarchical clustering algorithm which uses the inter-connectivity and the closeness of clusters to group structurally equivalent pages in an effective fashion. In addition, we address the preliminaries of the proposed blockmodeling and the theoretical foundations of HCUBE clustering algorithm. In order to improve the efficiency of HCUBE, we optimize it by reducing its time complexity from O(|V|2)O(|V|2) to O(|V|2/p)O(|V|2/p), where p is a constant representing the number of initial partitions. Finally, we conduct experiments on real data and the results show that HCUBE is effective at partitioning web social networks compared to the Chameleon and k-means algorithms.