Fuzzy Graphs are used for analyzing and modeling levels of informationin real-time systems (simple or complex networks). A community(network) is formed when human eProfiles (nodes) have links (edges)and interactions with each other. Considering multiple medium ofcommunications like email, chatting and short message service (SMS)in the network, it will make the graph more complex (dense graph orforest). To address this issue in this paper analyzes those humancommunities with the help of fuzzy graphs and highlights the status ofindividuals in a human community. Max-Min Composition (fuzzyrelation) was applied along with statistical analysis on fuzzy graphs ofhuman community. Interaction Index (II) is used to estimate the intensityof communication and Role Index (RI) determine the participation statusof individual in a human community. All this analysis will be used inour research and development of Community Algorithm, which will beused as a tool that will help in identifying, analyzing, manipulating,monitoring, and transforming human communities based on humaneProfiles.


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    Title :

    Analyzing Human Communities using Fuzzy Graphs


    Contributors:

    Publication date :

    2016-03-09


    Remarks:

    Pakistan Journal of Engineering, Technology & Science; Vol 4, No 1 (2014) ; 2224-2333 ; 2222-9930



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629