Mohammad Mahdi Arsanjani - Computer Engineering Department Iran University of Science and TechnologyTehran, Iran
Mohammad Reza Kangavari - Computer Engineering Department Iran University of Science and TechnologyTehran, Iran

Nowadays graphs are widely used to model data including network, web, software and even chemical and biological data analysis. In all of these cases, graph mining techniques are ubiquitous to extract novel information from graph structured data. One of the main challenges here is to understand the characteristics of large graphs and finding the interested information on them. Really, graph summarization techniques are very useful to solve this challenge. In this article, we overview the definitions, methods and applications of graph summarization which is constructing a simpler and smaller graphs from massive graphs such that the main characteristics and information of the original one are preserved. However, we first review the most important concepts about graph, graph mining and graph summarization. Then we propose a categorization for graph summarizing as structural and semantical summarization. In addition, we present a survey on some useful graph summarization methods regarding to proposed categorization. Finally, we introduce some applications of graph summarization including software engineering, network analysis, web and document analysis.

Mining; Summarization