Higher order clustering coefficients in Barabasi-Albert networks

Details Higher order clustering coefficients in Barabasi-Albert networks Higher order clustering coefficients in Barabasi-Albert networks

2002-12-11

arxiv.org/abs/cond-mat/0212237v1

Physica A 316, 2002, pp. 688-694

Physics

Condensed Matter

Disordered Systems and Neural Networks

10 pages, 4 figures

Scientific paper

10.1016/S0378-4371(02)01336-5

Higher order clustering coefficients $C(x)$ are introduced for random networks. The coefficients express probabilities that the shortest distance between any two nearest neighbours of a certain vertex $i$ equals $x$, when one neglects all paths crossing the node $i$. Using $C(x)$ we found that in the Barab\'{a}si-Albert (BA) model the average shortest path length in a node's neighbourhood is smaller than the equivalent quantity of the whole network and the remainder depends only on the network parameter $m$. Our results show that small values of the standard clustering coefficient in large BA networks are due to random character of the nearest neighbourhood of vertices in such networks.

Affiliated with

Also associated with

No associations

Rating

Higher order clustering coefficients in Barabasi-Albert 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 Higher order clustering coefficients in Barabasi-Albert networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Higher order clustering coefficients in Barabasi-Albert networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-637891