Subgraph mining in a large graph: A review

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249920" target="_blank" >RIV/61989100:27240/22:10249920 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/22:10249920
Result on the web
<a href="https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1454?casa_token=dPl7lX0ptm0AAAAA%3ANj0aa5N4eL1DtmOACnI_MNgIb3vgcbuV8dAJhaWZUkR5Gii5sPF7ah9AFCdIUijJx2-d4zyFqZlWCrM" target="_blank" >https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1454?casa_token=dPl7lX0ptm0AAAAA%3ANj0aa5N4eL1DtmOACnI_MNgIb3vgcbuV8dAJhaWZUkR5Gii5sPF7ah9AFCdIUijJx2-d4zyFqZlWCrM</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/widm.1454" target="_blank" >10.1002/widm.1454</a>

Result language
angličtina
Original language name
Subgraph mining in a large graph: A review
Original language description
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 large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future. This article is categorized under: Algorithmic Development > Association Rules Algorithmic Development > Structure Discovery
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
ISSN
1942-4787
e-ISSN
1942-4795
Volume of the periodical
12
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
—
UT code for WoS article
000765699100001
EID of the result in the Scopus database
—

Similar results(10)