Currently, there has been an increase in the use of frequent approximate subgraph (FAS) mining for different applications like graph classification. In graph classification tasks, FAS mining algorithms over graph collections have achieved good results, specially those algorithms that allow distortions between labels, keeping the graph topology. However, there are some applications where multi-g...

Frequent graph mining is one of famous data mining fields that receive the most attention, and its importance has been raised continually as recent databases in the real world become more complicated. Weighted frequent graph mining is an approach for applying importance of objects in the real world to the graph mining, and numerous studies related to this have been conducted so far. However, al...

Frequent graph pattern mining is one of the most interesting areas in data mining, and many researchers have developed a variety of approaches by suggesting efficient, useful mining techniques by integration of fundamental graph mining with other advanced mining works. However, previous graph mining approaches have faced fatal problems that cannot consider important characteristics in the real ...

Most of graph pattern mining algorithms focus on finding frequent subgraphs and its compact representations, such as closed frequent subgraphs and maximal frequent subgraphs. However, little attention has been paid to mining graph patterns with user-specified significance measure. In this paper, we study a new problem of mining top-k graph patterns that jointly maximize some significance measur...

Mining frequent subgraphs is a basic activity that plays an important role in mining graph data. In this paper an algorithm is proposed to find frequent subgraphs in a single large graph that has applications such as protein interactions, social networks, web interactions. One of the key operations required by any frequent subgraph discovery algorithm is to perform graph isomorphism. The propos...

Graph mining and management 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, software bug localization and computer networking. Different applications result in graphs of different sizes and complexities. Correspondingly, the applications have different requirements for the underlyi...

Mining chemical compounds in silico has drawn increasing attention from both academia and pharmaceutical industry due to its effectiveness in aiding the drug discovery process. Since graphs are the natural representation for chemical compounds, most of the mining algorithms focus on mining chemical graphs. Chemical graph mining approaches have many applications in the drug discovery process tha...

Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statisti...