Graph mining has become a well-established discipline within
the domain of data mining. It has received much interest over the last decade
as advances in computer hardware have provided the processing power to enable
large-scale graph data mining to be conducted. Frequent subgraph mining (FSM)
plays a very significant role in graph mining, attracting a great deal of
attention in different domains, such as Bioinformatics, web data mining and
social networks. Research on FSM started around 1994, but it has become popular
since 2008 when the size of graphs in different domains became relatively
large. Several techniques have been proposed in the literature for the FSM
problem. In this paper, we reviewed some recently presented FSM techniques and
investigated some challenges and future research directions. A few surveys have
been conducted to review different techniques for the FSM problem. However,
existing surveys highlighted only the methodology adopted for frequent subgraph
discover but did not critically review their shortcomings. Also, the existing
surveys/reviews are not comprehensive enough and are insufficient to highlight
the challenges in the FSM domain along with their possible solutions. Consequently, there is a need for a survey
that incorporates recent techniques. Therefore, this study aimed to comprehensively
survey the current research in the field of FSM. In this survey the key
characteristics of each FSM approach are analyzed, such as the proposed
methodology, which type of graph structure is used, applied similarity
measures, metrics used for measuring the performance, uncertainty handled or
not, used data set, capabilities of the techniques, evidence used and
limitations of these techniques. As a result, this paper identifies the current
status of the research in the FSM, and future research directions in this field
are determined based on opportunities and several open issues in FSM
domination. These research directions, facilitate the exploration of the domain
and the development of optimal techniques to address FSM.
Author(s) Details:
Saif Ur Rehman,
University Institute of Information Technology, PMAS-Arid
Agriculture Universty, Rawalpinid, Pakistan.
Muhammad
Ibrahim Khalil,
University
Institute of Information Technology, PMAS-Arid Agriculture Universty,
Rawalpinid, Pakistan.
Mahwish Kundi,
Maynooth International Engineering College, Maynooth University, Co.
Kildare, Ireland.
Tahani AlSaedi,
Applied College, Taibah University, Saudi Arabia.
Please see the link here: https://stm.bookpi.org/RUMCS-V4/article/view/14099
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