Abstract:

Data mining consists of many data analysis techniques. Its primary purpose is to discover hidden and useful relationships between data from a very large collection. Graph mining, which has gained in importance in recent decades, is one of the innovative approaches to explore a structured data set represented by graphs and networks [20]. It is used in various fields, such as bioinformatics, chemical reactions, program flow structures, computer networks, social networks, etc. Different methods of data mining are used to extract graph-based data and perform useful analysis of this data. Different approaches to graph and network (weighted graphs) exploration are proposed in the literature [20], [24]. Each of these approaches is based on classification, clustering, or decision trees. This paper focuses on a specific group of methods of graph and network mining related to the so-called community detection.