Clustering coefficient is the measure of how tightly vertices are bounded in a network. The Triangle Counting problem is at the core of clustering coefficient computation. We present a new technique for implementing clustering coefficient algorithm on GPUs. It relies on neighbour list being sorted with respect to vertex ID. The algorithm can process very large graphs not seen in the literature ...

Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values....

Social and other networks have been shown empirically to exhibit high edge clustering—that is, the density of local neighborhoods, as measured by the clustering coefficient, is often much larger than the overall edge density of the network. In social networks, a desire for tightknit circles of friendships — the colloquial “social clique” — is often cited as the primary driver of such structure....

An important source of high clustering coefficient in real-world networks is transitivity. However, existing approaches for modeling transitivity suffer from at least one of the following problems: i) they produce graphs from a specific class like bipartite graphs, ii) they do not give an analytical argument for the high clustering coefficient of the model, and iii) their clustering coefficient...

Gene expression levels are often measured consecutively in time through microarray experiments to detect cellular processes underlying regulatory effects observed and to assign functionality to genes whose function is yet unknown. Clustering methods allow us to group genes that show similar time-course expression profiles and that are thus likely to be co-regulated. The correlation coefficient,...

Many networks exhibit the small-world property of the neighborhood connectivity being higher than in comparable random networks. However, the standard measure of local neighborhood clustering is typically not defined if a node has one or no neighbor. In such cases, local clustering has traditionally been set to zero and this value influenced the global clustering coefficient. Such a procedure l...