Skip to search formSkip to main contentSkip to account menu

Clustering coefficient

Known as: Clustering 
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in… 
Wikipedia (opens in a new tab)

Related topics

Related topics

33 relations

Broader (2)

Algebraic graph theoryNetwork theory
Average path lengthBarabási–Albert modelClimate as complex networksClique (graph theory)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Towards understanding cyberbullying behavior in a semi-anonymous social network
Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones… 
Highly Cited
2013
Highly Cited
2013
Estimating clustering coefficients and size of social networks via random walk
Online social networks have become a major force in today's society and economy. The largest of today's social networks may have… 
Highly Cited
2012
Highly Cited
2012
Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge
Highly Cited
2012
Highly Cited
2012
Density-based Projected Clustering over High Dimensional Data Streams
Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being… 
Highly Cited
2012
Highly Cited
2012
The robustness of interdependent clustered networks
It was recently found that cascading failures can cause the abrupt breakdown of a system of interdependent networks. Using the… 
Highly Cited
2011
Highly Cited
2011
Link prediction in complex networks: A local naïve Bayes model
The common-neighbor–based method is simple yet effective to predict missing links, which assume that two nodes are more likely to… 
Highly Cited
2010
Highly Cited
2010
Power Iteration Clustering
We present a simple and scalable graph clustering method called power iteration clustering (PIC). PIC finds a very low… 
Highly Cited
2009
Highly Cited
2009
A Novel Density-Based Clustering Framework by Using Level Set Method
In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data… 
Highly Cited
2007
Highly Cited
2007
The entropy of randomized network ensembles
Randomized network ensembles are the null models of real networks and are extensively used to compare a real system to a null… 
Highly Cited
2000
Highly Cited
2000
Deterministic small-world communication networks
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)