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  1. 1
    A LINEAR-PROGRAMMING APPROACH FOR THE WEIGHTED GRAPH MATCHING PROBLEM

    A LINEAR-PROGRAMMING APPROACH FOR THE WEIGHTED GRAPH MATCHING PROBLEM by Al-Mohamad, HA

    Published 2020
    “…The complexity of the proposed algorithm is polynomial time, and it is O(n6 L) for matching graphs of size n. …”
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  2. 2
    Maximal clique enumeration. (c2007)

    Maximal clique enumeration. (c2007) by Barghout, Hamed

    Published 2007
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    masterThesis
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  3. 3
    A linear programming approach for the weighted graph matchingproblem

    A linear programming approach for the weighted graph matchingproblem by Almohamad, H.A.

    Published 1993
    “…The complexity of the proposed algorithm is polynomial time, and it is O(n 6L) for matching graphs of size n. …”
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  4. 4
    A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments

    A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments by Barada, Hassan

    Published 2020
    “…Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs representing the application tasks. The performance of SE is compared with a genetic algorithm approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms. r 2003 Elsevier Science Ltd. …”
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  5. 5
    Modular-width

    Modular-width by Abu-Khzam, Faisal N.

    Published 2017
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    conferenceObject
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  6. 6
    Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS

    Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS by Tekli, Joe

    Published 2018
    “…., considering the lexical and semantic similarities/disparities when matching user query and data index terms. To do so, we design and construct a semantic-aware inverted index structure called SemIndex, extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. …”
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