Large graphs are often used to simulate and model complex systems in various research
and application fields. Because of its importance, frequent subgraph mining (FSM) in single …

Mining frequent subgraphs is an interesting and important problem in the graph mining field,
in that mining frequent subgraphs from a single large graph has been strongly developed …

The algorithm that allows based on the analysis of monitoring historical data to predict the
state of the network has been proposed. It is aimed to evaluate the state of the network using …

Graph data-based mining is vital in various fields, such as business management, chemistry,
and social networks. Frequency-based frameworks have limitations regarding large mining …

Interesting pattern discovery is an important topic in data mining research. Many different
definitions have been proposed to describe whether a pattern is interesting. Among these …

In humans, the amygdala and piriform cortex are 2 important brain structures involved in
hedonic odor processing. Although the affective processing of odors in these 2 structures …

In this paper, we propose a novel model to recommend possible research peers to a user
efficiently. We model all the authors along with their attributes as an attributed graph and …

Data clustering, local pattern mining, and community detection in graphs are three mature
areas of data mining and machine learning. In recent years, attributed subgraph mining has …

Frequent subgraph mining (FSM) is an interesting research field and has attracted a lot of
attention from many researchers in recent years, in which closed subgraph mining is a new …

We address the problem of pattern discovery in vertex-attributed graphs. This kind of
structure consists of a graph augmented with attributes associated to vertices. Vertex …