Search Results - Nirmala, Parisutham

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  1. 1
    How do centrality measures help to predict similarity patterns in molecular chemical structural graphs?

    How do centrality measures help to predict similarity patterns in molecular chemical structural graphs? by Parisutham, Nirmala

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  2. 2
    Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs

    Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs by Parisutham, Nirmala, Rethnasamy, Nadarajan

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  3. 3
    Cumulative centrality index: Centrality measures based ranking technique for molecular chemical structural graphs

    Cumulative centrality index: Centrality measures based ranking technique for molecular chemical structural graphs by Nirmala, Parisutham, Nadarajan, Rethnasamy

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  4. 4
    TCPP-2PPIN: trustworthy centrality prediction paradigm for analyzing two protein–protein interaction networks using centrality measures and graph theory concepts

    TCPP-2PPIN: trustworthy centrality prediction paradigm for analyzing two protein–protein interaction networks using centrality measures and graph theory concepts by Parisutham, Nirmala, Deep, Blesson, Aswin, G.

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  5. 5
    Vertex cover-based binary tree algorithm to detect all maximum common induced subgraphs in large communication networks

    Vertex cover-based binary tree algorithm to detect all maximum common induced subgraphs in large communication networks by Nirmala, Parisutham, Lekshmi, Ramasubramony Sulochana, Nadarajan, Rethnasamy

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  6. 6
    An algorithm to search edge relaxed query graph with minimum support threshold in large communication networks

    An algorithm to search edge relaxed query graph with minimum support threshold in large communication networks by Parisutham, Nirmala, Rethnasamy, Nadarajan

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  7. 7
    Centrality measures-based algorithm to visualize a maximal common induced subgraph in large communication networks

    Centrality measures-based algorithm to visualize a maximal common induced subgraph in large communication networks by Nirmala, Parisutham, Ramasubramony Sulochana, Lekshmi, Rethnasamy, Nadarajan

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