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                <datestamp>2019-05-29T09:41:09Z</datestamp>
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                    <dim:field mdschema="dc" element="title" lang="en">Managing Big Data Using Fuzzy Sets by Directed Graph Node Similarity</dim:field>
                    <dim:field mdschema="dc" element="date" qualifier="issued">2017</dim:field>
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                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:27196" confidence="-1">M. Jocić</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:27197" confidence="-1">E. Pap</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:27198" confidence="-1">A. Szakál</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="etfid:794" confidence="-1">Đ. Obradović</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="etfid:653" confidence="-1">З. Коњовић</dim:field>
                    <dim:field mdschema="dc" element="description" qualifier="abstract">This paper proposes a novel algorithm for discovering similar nodes in very large directed graphs, with millions of nodes with billions of connections, which is based on the fuzzy set theory. The required input is 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. © 2017, Budapest Tech Polytechnical Institution. All rights reserved.</dim:field>
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                    <dim:field mdschema="dc" element="identifier" qualifier="doi">10.12700/APH.14.2.2017.2.10</dim:field>
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                    <dim:field mdschema="dc" element="identifier" qualifier="issn">1785-8860</dim:field>
                    <dim:field mdschema="dc" element="source">Acta Polytechnica Hungarica</dim:field>
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