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                <identifier>ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:4554</identifier>
                <datestamp>2016-10-27T17:55:55Z</datestamp>
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                    <dim:field mdschema="dc" element="title" lang="en">Managing Big Data by Directed Graph Node Similarity</dim:field>
                    <dim:field mdschema="dc" element="date" qualifier="issued">2016</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/4554</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0003-0719-4701" confidence="-1">E. Pap</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="etfid:653" confidence="-1">Z. Konjović</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:15963" confidence="-1">A. Szakal</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:15964" confidence="-1">D. Obradović</dim:field>
                    <dim:field mdschema="dc" element="description" qualifier="abstract">This paper shows a novel algorithm based on the
theory of fuzzy sets for discovering similar nodes in very large
directed graphs (millions of nodes with billions of
connections), if provided with a sample of representative
nodes that are highly affiliated with some feature. This
approach is practically verified on Twitter social network
case study to discover influential Twitter users in the field of
science.</dim:field>
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                    <dim:field mdschema="dc" element="source">17th International Symposium on Computational Intelligence and Informatics CINTI 2016</dim:field>
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