期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：3
页码：501-505
语种：English
出版社：Ayushmaan Technologies
摘要：Collective behavior of an individual in social network given a good path to the researcher and study of this gather the information about an individual behavior in an effective way. Many social networks generate a huge amount of data which gives an opportunity to study the collective behavior. In this work, our aim is to predict collective behavior in social media. In spite of the heterogeneity of network and geographical area we can infer the behavior of an unobserved individual perfectly. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. Which approach can efficiently handle networks of different zone and give a comparatively good result.
