Modeling Objects and Processes in GIS by Fuzzy Temporal Graphs

Bozhenyuk, Alexander; Belyakov, Stanislav; Knyazeva, Margarita

doi:10.1007/978-3-030-47124-8_22

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…More specifically, the development of science and technology provides various tools of graph mining for GIS (Daniel et al, 2020). An important concept of a strongly connected fuzzy temporal graph and a fuzzy set of strong connectivity is proposed in Bozhenyuk et al (2021). A fuzzy temporal graph is also offered to use as a model in GIS.…”

Section: Applications Of Fsmmentioning

confidence: 99%

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Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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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.

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Section: Applications Of Fsmmentioning

confidence: 99%

“…A fuzzy temporal graph is also offered to use as a model in GIS. A method for finding a fuzzy set (Bozhenyuk et al, 2021) with strong connectivity has been proposed, meanwhile, an example was introduced for a fuzzy set of strong connections in a fuzzy temporal graph (Bozhenyuk et al, 2021).…”

Section: Applications Of Fsmmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

Snášel

et al. 2022

WIREs Data Min & Knowl

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“…Temporal graphs have applications in modeling a wide range of phenomena, such as communication networks, computational biology, transportation networks, the spread of viruses, social networks, etc. [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Path Algorithms for Contact Sequence Temporal Graphs

Gheibi,

Banerjee,

Ranka

et al. 2024

Algorithms

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This paper proposes a new time-respecting graph (TRG) representation for contact sequence temporal graphs. Our representation is more memory-efficient than previously proposed representations and has run-time advantages over the ordered sequence of edges (OSE) representation, which is faster than other known representations. While our proposed representation clearly outperforms the OSE representation for shallow neighborhood search problems, it is not evident that it does so for different problems. We demonstrate the competitiveness of our TRG representation for the single-source all-destinations fastest, min-hop, shortest, and foremost paths problems.

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“…Such graphs [27,28] were called intuitionistic graphs of the first kind. For example, when reflecting complex interactions and relationships between elements in a geographic information system (GIS), an intuitionistic fuzzy graph of the first kind formalizes fuzzy relations and connections between crisp objects, so that objects themselves remain static and unchanged [29,30]. The practical relevance of such an approach can be interpreted as a problem of optimal service centers allocation (finding domination set in the intuitionistic fuzzy graph) within the railway network transportation system.…”

Section: Introductionmentioning

confidence: 99%

On Computing Domination Set in Intuitionistic Fuzzy Graph

Bozhenyuk¹,

Belyakov²,

Knyazeva³

et al. 2021

IJCIS

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In this paper, the concept of minimal intuitionistic dominating vertex subset of an intuitionistic fuzzy graph was considered, and on its basis, the notion of a domination set as an invariant of the intuitionistic fuzzy graph was introduced. A method and an algorithm for finding all minimal intuitionistic dominating vertex subset and domination set was proposed. This method is the generalization of Maghout's method for fuzzy graphs. The example of finding the domination set of the intuitionistic fuzzy graph were considered as well.

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Modeling Objects and Processes in GIS by Fuzzy Temporal Graphs

Cited by 7 publications

References 2 publications

Subgraph mining in a large graph: A review

Subgraph mining in a large graph: A review

Path Algorithms for Contact Sequence Temporal Graphs

On Computing Domination Set in Intuitionistic Fuzzy Graph

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