Attribute Driven Temporal Active Local Online Community Detection

This research investigates how the behavior of online social users' and topical activeness vary with time and how these parameters can be employed in order to improve the quality of the detected online local community. For a given input query, comprising a query node (user) and a set of attributes, this paper intends to find a densely connected community in which community members are temporally similar in terms of their activities related to the query attributes. To address the proposed problem, we develop a temporal activity biased weight model which assigns higher weight to users' recent activities and develop an algorithm to search effective community. The effectiveness of the proposed methodology is justified using three benchmark datasets.