Graph data is found in numerous domains such as for the analysis of social networks,
sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to …

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 …

Frequent subgraph mining is a popular data mining task, which consists of finding all
subgraphs that appear in at least minsup graphs of a graph database. An important …

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 …

Frequent pattern mining (FPM), whose goal is to identify patterns with appearance
frequencies above a specified support threshold on a large graph, has attracted increasing …

Frequent pattern mining (FPM) has played an important role in many graph domains, such
as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of …

Frequent itemsets (FIs) mining is a prime research area in association rule mining. The
customary techniques find FIs or its variants on the basis of either support threshold value or …

In this report, we list the algorithms proposed in the literature of Frequent Subgraph Mining
(FSM) in Centralized Graph Transaction Databases. We categorize FSM algorithms in four …

This chapter bridges the gap between pattern mining in pattern recognition and motif
discovery from protein 3D‐structures. It shows how to use graph mining techniques to mine …