A Survey on Assorted Approaches to Graph Data Mining

Abstract

Graph mining has become a popular area of research in recent years because of its numerous applications in a wide variety of practical fields, including computational biology, sociology, software bug localization, keyword search, and computer networking. Different applications result in graphs of different sizes and complexities. Graph mining is an important tool to transform the graphical data into graphical information. We investigate recurring patterns in real-world graphs, to gain a deeper understanding of their structure. We can extract normal and abnormal subgraphs thereby detecting suspicious nodes and outliers in the existing graphs. In this paper we present a survey of various approaches to mine the graphs. These are used to extract patterns, trends, classes, and clusters from graphs.