Graph partitioning and visualization in graph mining: a survey

Graph mining is a process of obtaining one or more sub-graphs and has been a very attractive research topic over the last two decades. It has found many practical applications dealing with real world problems in variety of domains like Social Network Analysis, Designing of Computer Networks, Study of Chemical Reactions, Bio Informatics, Program Flow Structures, Image Processing, Enterprise data, Sparse Matrix ordering and many more. For these applications, many graph classification and Graph Clustering algorithms are evolved. This paper presents a comprehensive survey of published work in Graph Mining by grouping them in a broad taxonomy. For each of these groups in the taxonomy, the basic concepts of the algorithms are covered in detail by mentioning the contributions of various authors to the basic concepts of each group. Furthermore, common issues in graph mining algorithms, such as clustering, partitioning, visualization of graphs, are also elaborated. Standard datasets available for graph mining are stated as well.