Scalable community discovery on textual data with relations

Every piece of textual data is generated as a method to convey its authors' opinion regarding specific topics. Authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. Thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. Although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. First, few of them consider both textual attributes as well as relations. Second, scalability remains a significant issue for large-scale datasets. Additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. Motivated by the aforementioned observations, a hierarchical community model is proposed in the paper which distinguishes community cores from affiliated members. We present our efforts to develop a scalable...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Huajing Li, Zaiqing Nie, Wang-Chien Lee, C. Lee Giles, Ji-Rong Wen
• Keywords: CIKM 2008, Community Discovery, Document Corpus, Information Management, Initial Parameters