CLUSTER ANALYSIS OF A SCALE-FREE NETWORK
In this paper we present an analysis of a cluster based inference in a particular computer network. The faculty forum on a real community server, where students and stuff share their knowledge and experiences, is used for this purpose. In order to better understand the structure of the network, we represent it as a graph, where vertices are represented by the members of the forum and the edges act as the links between the forum posts. As in many similar systems, this forum is organized in threads that are divided into sections (subjects), and sections are divided into groups (academic years). It is shown that the resulting network exhibits a scale-free distribution with large clustering coefficients following the small-world properties. As the clusters hold some important information about the nature of the network, we developed a special software agent that explores the background SQL database and automatically acquires the relevant information. Based on this data, detailed information including the graphs degree distribution, clustering coefficient, Laplacian, and normalized Laplacian eigenvectors and average distance are calculated. The resulting analysis gives us a better understanding of the nature of this particular network, which can be valuable information for the administrators.