An efficient and scalable approach for mining subgraphs in a single large graph
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10249926" target="_blank" >RIV/61989100:27740/22:10249926 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/22:10249926
Result on the web
<a href="https://link.springer.com/article/10.1007/s10489-022-03164-5" target="_blank" >https://link.springer.com/article/10.1007/s10489-022-03164-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-022-03164-5" target="_blank" >10.1007/s10489-022-03164-5</a>
Alternative languages
Result language
angličtina
Original language name
An efficient and scalable approach for mining subgraphs in a single large graph
Original language description
In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the underlying graphs for these systems are very large. Algorithms for mining all frequent subgraphs from a single large graph have attracted much attention and been studied in more detail lately. Mining frequent subgraphs is important, and defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. Among frequent subgraph algorithms, GraMi is considered as the state-of-the-art approach. However, GraMi has a huge search space, and therefore still needs a lot of time and memory to process a large graph. In this paper, we propose two effective strategies to balance and reduce the search space of GraMi, which can decrease the number of candidate subgraphs generated, with early pruning of a large portion of the domain for each candidate. Our experiments were performed on four real datasets and the results show that the performance of our balancing GraMi is better than those of the original algorithm GraMi and the optimized version SoGraMi.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Intelligence
ISSN
0924-669X
e-ISSN
1573-7497
Volume of the periodical
52
Issue of the periodical within the volume
15
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
nestrankovano
UT code for WoS article
000778916300004
EID of the result in the Scopus database
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