Elimination based algorithm for link prediction on social networks
Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sharma, Upasana [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 6(2014), 1 vom: 01. März, Seite 78-82 |
|---|---|
| Übergeordnetes Werk: |
volume:6 ; year:2014 ; number:1 ; day:01 ; month:03 ; pages:78-82 |
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10.1007/s13198-014-0245-2 |
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| 520 | |a Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. | ||
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10.1007/s13198-014-0245-2 doi (DE-627)SPR031248578 (SPR)s13198-014-0245-2-e DE-627 ger DE-627 rakwb eng Sharma, Upasana verfasserin aut Elimination based algorithm for link prediction on social networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. Link prediction (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Algorithms (dpeaa)DE-He213 Sharma, Dolly aut Khatri, Sunil Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 6(2014), 1 vom: 01. März, Seite 78-82 (DE-627)SPR031222420 nnns volume:6 year:2014 number:1 day:01 month:03 pages:78-82 https://dx.doi.org/10.1007/s13198-014-0245-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 6 2014 1 01 03 78-82 |
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10.1007/s13198-014-0245-2 doi (DE-627)SPR031248578 (SPR)s13198-014-0245-2-e DE-627 ger DE-627 rakwb eng Sharma, Upasana verfasserin aut Elimination based algorithm for link prediction on social networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. Link prediction (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Algorithms (dpeaa)DE-He213 Sharma, Dolly aut Khatri, Sunil Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 6(2014), 1 vom: 01. März, Seite 78-82 (DE-627)SPR031222420 nnns volume:6 year:2014 number:1 day:01 month:03 pages:78-82 https://dx.doi.org/10.1007/s13198-014-0245-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 6 2014 1 01 03 78-82 |
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10.1007/s13198-014-0245-2 doi (DE-627)SPR031248578 (SPR)s13198-014-0245-2-e DE-627 ger DE-627 rakwb eng Sharma, Upasana verfasserin aut Elimination based algorithm for link prediction on social networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. Link prediction (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Algorithms (dpeaa)DE-He213 Sharma, Dolly aut Khatri, Sunil Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 6(2014), 1 vom: 01. März, Seite 78-82 (DE-627)SPR031222420 nnns volume:6 year:2014 number:1 day:01 month:03 pages:78-82 https://dx.doi.org/10.1007/s13198-014-0245-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 6 2014 1 01 03 78-82 |
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10.1007/s13198-014-0245-2 doi (DE-627)SPR031248578 (SPR)s13198-014-0245-2-e DE-627 ger DE-627 rakwb eng Sharma, Upasana verfasserin aut Elimination based algorithm for link prediction on social networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. Link prediction (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Algorithms (dpeaa)DE-He213 Sharma, Dolly aut Khatri, Sunil Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 6(2014), 1 vom: 01. März, Seite 78-82 (DE-627)SPR031222420 nnns volume:6 year:2014 number:1 day:01 month:03 pages:78-82 https://dx.doi.org/10.1007/s13198-014-0245-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 6 2014 1 01 03 78-82 |
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10.1007/s13198-014-0245-2 doi (DE-627)SPR031248578 (SPR)s13198-014-0245-2-e DE-627 ger DE-627 rakwb eng Sharma, Upasana verfasserin aut Elimination based algorithm for link prediction on social networks 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. Link prediction (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Algorithms (dpeaa)DE-He213 Sharma, Dolly aut Khatri, Sunil Kumar aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 6(2014), 1 vom: 01. März, Seite 78-82 (DE-627)SPR031222420 nnns volume:6 year:2014 number:1 day:01 month:03 pages:78-82 https://dx.doi.org/10.1007/s13198-014-0245-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 6 2014 1 01 03 78-82 |
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Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR031248578</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331061313.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13198-014-0245-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR031248578</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13198-014-0245-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sharma, Upasana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Elimination based algorithm for link prediction on social networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Link prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sharma, Dolly</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Khatri, Sunil Kumar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International Journal of Systems Assurance Engineering and Management</subfield><subfield code="d">Springer-Verlag, 2010</subfield><subfield code="g">6(2014), 1 vom: 01. März, Seite 78-82</subfield><subfield code="w">(DE-627)SPR031222420</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:6</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">day:01</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:78-82</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13198-014-0245-2</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">6</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="b">01</subfield><subfield code="c">03</subfield><subfield code="h">78-82</subfield></datafield></record></collection>
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7.399989 |