Cart (Loading....) | Create Account
Close category search window
 

Using evolutionary algorithms for defining the sampling policy of complex n-partite networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Goldstein, M.L. ; Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA ; Yen, G.G.

N-partite networks are natural representations of complex multientity databases. However, processing these networks can be a highly memory and computation-intensive task, especially when positive correlation exists between the degrees of vertices from different partitions. In order to improve the scalability of this process, this paper proposes two algorithms that make use of sampling for obtaining less expensive approximate results. The first algorithm is optimal for obtaining homogeneous discovery rates with a low memory requirement, but can be very slow in cases where the combined branching factor of these networks is too large. A second algorithm that incorporates concepts from evolutionary computation aims toward dealing with this slow convergence in the case when it is more interesting to increase approximation convergence speed of elements with high feature values. This algorithm makes use of the positive correlation between "local" branching factors and the feature values. Two applications examples are demonstrated in searching for most influential authors in collections of journal articles and in analyzing most active earthquake regions from a collection of earthquake events.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:17 ,  Issue: 6 )

Date of Publication:

June 2005

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.