Mining of diverse social entities from linked data

Abstract

Nowadays, high volumes of valuable data can be easily generated or collected from various data sources at high velocity. As these data are often related or linked, they form a web of linked data. Examples include semantic web and social web. The social web captures social relationships that link people (i.e., social entities) through the World Wide Web. Due to the popularity of social networking sites, more people have joined and more online social interactions have taken place. With a huge number of social entities (e.g., users or friends in social networks), it becomes important to analyze high volumes of linked data and discover those diverse social entities. In this paper, we present (i) a tree-based mining algorithm called DF-growth, along with (ii) its related data structure called DF-tree, which allow users to effectively and efficiently mine diverse friends from social networks. Results of our experimental evaluation showed both the time and space-efficiency of our scalable DF-growth algorithm, which makes good use of the DF-tree structure.