Estimating Growth Parameters for the Drosophila Melanogaster Protein Interaction Network by a Network Comparison Method Based on Breadth-First Search
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.
Xianchuang Su Xiaogang Jin Yong Min Yixiao Li
Institute of Artificial Intelligence College of Computer Science Zhejiang University Hangzhou, China 310027 Ningbo Institute of Technology, Zhejiang University Ningbo, China 315100
国际会议
The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)
杭州
英文
344-348
2010-11-15（万方平台首次上网日期，不代表论文的发表时间）