Authors: Anand Gupta; Hardeo Kumar Thakur; Anshul Garg
Addresses: Department of Computer Engineering, Netaji Subhas Institute of Technology, New Delhi, India ' Department of Computer Engineering, Netaji Subhas Institute of Technology, New Delhi, India ' Department of Information Technology, Netaji Subhas Institute of Technology, New Delhi, India
Abstract: Graph is one of the media to represent and summarise interactions in a time varying network. Often, interactions repeat after a fixed interval of time and exhibit temporal periodicity. Existing algorithms focus either on the structure or on the weight of periodic interactions individually. But, for instance, a stock analyst requires evidence of both structure and weight (here, price) of the stock pairs to make prediction and discovers information about the profit producing stocks and the actual profit. On performing experiments using existing algorithms explicitly, it is observed that the efficiency is lost in such applications. Hence, in this paper, we provide an efficient framework based on available algorithms to mine periodic patterns both on structure and weight in a weighted dynamic network. The proposed framework consists of a mapping between interactions that are periodic on structure and weight. We have performed experiments on synthetic and real world datasets. The results validate the scalability and practical feasibility of the proposed framework.
Keywords: dynamic graph; periodic graph; frequent graph; weighted graph.
International Journal of Intelligent Systems Design and Computing, 2017 Vol.1 No.3/4, pp.319 - 338
Received: 23 Mar 2015
Accepted: 30 Sep 2015
Published online: 26 Mar 2018 *