Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2007-05-29
New J. Phys. 9 (2007) 311
Physics
Condensed Matter
Disordered Systems and Neural Networks
9 pages, 4 figures
Scientific paper
10.1088/1367-2630/9/9/311
This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.
Andrade Roberto F. S.
Costa Luciano da F.
No associations
LandOfFree
What are the Best Hierarchical Descriptors for Complex 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 What are the Best Hierarchical Descriptors for Complex Networks?, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and What are the Best Hierarchical Descriptors for Complex Networks? will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-499611