Extent prediction of the information and influence propagation in online social networks
We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verif...
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| Veröffentlicht in: | Computational and mathematical organization theory 2021-06, Vol.27 (2), p.195-230 |
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| creator | Ortiz-Gaona, Raúl M. Postigo-Boix, Marcos Melús-Moreno, José L. |
| description | We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models
Linear Threshold
and
Independent Cascade
, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks. |
| doi_str_mv | 10.1007/s10588-020-09309-6 |
| format | Article |
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Linear Threshold
and
Independent Cascade
, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks.</description><identifier>ISSN: 1381-298X</identifier><identifier>EISSN: 1572-9346</identifier><identifier>DOI: 10.1007/s10588-020-09309-6</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Affinity ; Artificial Intelligence ; Business and Management ; Cellular telephony ; Computer networks ; Computer viruses ; Laboratory tests ; Management ; Mathematical models ; Messages ; Methodology of the Social Sciences ; Mobile computing ; Operations Research/Decision Theory ; Organization theory ; Propagation ; S.I.: Social Cyber-Security ; Social networks ; Sociology ; Telephony ; Viruses</subject><ispartof>Computational and mathematical organization theory, 2021-06, Vol.27 (2), p.195-230</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c371t-2de404cf4da6d13629571ebcedd7e9cb9bb21d0979cbe24d41e1fe6b0c9e65d73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10588-020-09309-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10588-020-09309-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Ortiz-Gaona, Raúl M.</creatorcontrib><creatorcontrib>Postigo-Boix, Marcos</creatorcontrib><creatorcontrib>Melús-Moreno, José L.</creatorcontrib><title>Extent prediction of the information and influence propagation in online social networks</title><title>Computational and mathematical organization theory</title><addtitle>Comput Math Organ Theory</addtitle><description>We present a new mathematical model that predicts the number of users informed and influenced by messages that are propagated in an online social network. Our model is based on a new way of quantifying the tie-strength, which in turn considers the affinity and relevance between nodes. We could verify that the messages to inform and influence, as well as their importance, produce different propagation behaviors in an online social network. We carried out laboratory tests with our model and with the baseline models
Linear Threshold
and
Independent Cascade
, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. 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Linear Threshold
and
Independent Cascade
, which are currently used in many scientific works. The results were evaluated by comparing them with empirical data. The tests show conclusively that the predictions of our model are notably more accurate and precise than the predictions of the baseline models. Our model can contribute to the development of models that maximize the propagation of messages; to predict the spread of viruses in computer networks, mobile telephony and online social networks.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10588-020-09309-6</doi><tpages>36</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Affinity Artificial Intelligence Business and Management Cellular telephony Computer networks Computer viruses Laboratory tests Management Mathematical models Messages Methodology of the Social Sciences Mobile computing Operations Research/Decision Theory Organization theory Propagation S.I.: Social Cyber-Security Social networks Sociology Telephony Viruses |
| title | Extent prediction of the information and influence propagation in online social networks |
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