By Topic

Estimating growth parameters for the Drosophila melanogaster protein interaction network by a network comparison method based on breadth-first search

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

4 Author(s)
Xianchuang Su ; Inst. of Artificial Intell., Zhejiang Univ., Hangzhou, China ; Xiaogang Jin ; Yong Min ; Yixiao Li

Availability of large-scale network data for real systems is enabling mathematical and computational methods to systematically model the formation of the networks. Various growth models are proposed to reproduce the structures of the real-world networks. Evaluating how well a model fits the network data is an outstanding challenge, since the structures of networks that have tens of thousands of vertices and edges are highly complex. We here use a trace curve, which is produced by a breadth-first search processing on the network, to characterize the structure of network. Because the trace curve is shaped by both the local and global structure of the network, it can be used to tell the subtle difference between networks. By comparing the curves of model network and real network data, we evaluate the fit of model to the data. The evaluation of fit subsequently can be used to estimate the growth parameters for real network, which are key factors affecting the growth of real system. We illustrate the power of this approach by estimating growth parameters for the Drosophila melanogaster protein interaction network.

Published in:

Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on

Date of Conference:

15-16 Nov. 2010