Reliability of optimal linear projection of growing scale-free networks

Details Reliability of optimal linear projection of growing scale-free networks Reliability of optimal linear projection of growing scale-free networks

2011-01-11

arxiv.org/abs/1101.2064v1

Physics

Data Analysis, Statistics and Probability

Scientific paper

Singular Value Decomposition (SVD) is a technique based on linear projection theory, which has been frequently used for data analysis. It constitutes an optimal (in the sense of least squares) decomposition of a matrix in the most relevant directions of the data variance. Usually, this information is used to reduce the dimensionality of the data set in a few principal projection directions, this is called Truncated Singular Value Decomposition (TSVD). In situations where the data is continuously changing the projection might become obsolete. Since the change rate of data can be fast, it is an interesting question whether the TSVD projection of the initial data is reliable. In the case of complex networks, this scenario is particularly important when considering network growth. Here we study the reliability of the TSVD projection of growing scale free networks, monitoring its evolution at global and local scales.

Affiliated with

Also associated with

No associations

Rating

Reliability of optimal linear projection of growing scale-free networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Reliability of optimal linear projection of growing scale-free networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Reliability of optimal linear projection of growing scale-free networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-160215