Periodic pattern mining in weighted dynamic networks
by Anand Gupta; Hardeo Kumar Thakur; Anshul Garg
International Journal of Intelligent Systems Design and Computing (IJISDC), Vol. 1, No. 3/4, 2017

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.

Online publication date: Fri, 30-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Design and Computing (IJISDC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com