A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks
Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence,...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1084795 |
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| author | Vanja Smailovic Vedran Podobnik Ignac Lovrek |
| author_facet | Vanja Smailovic Vedran Podobnik Ignac Lovrek |
| author_sort | Vanja Smailovic |
| collection | DOAJ |
| description | Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence, which depends on (i) the user’s positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for today’s social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a user’s social influence. |
| format | Article |
| id | doaj-art-9bdc823e2c664490aa032d39fab0eb76 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9bdc823e2c664490aa032d39fab0eb762025-08-20T02:18:25ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/10847951084795A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social NetworksVanja Smailovic0Vedran Podobnik1Ignac Lovrek2Sandvik Machining Solutions AB, Stockholm, SwedenSocial Networking and Computing Laboratory (socialLAB), Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaSocial Networking and Computing Laboratory (socialLAB), Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaOnline social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence, which depends on (i) the user’s positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for today’s social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a user’s social influence.http://dx.doi.org/10.1155/2018/1084795 |
| spellingShingle | Vanja Smailovic Vedran Podobnik Ignac Lovrek A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks Complexity |
| title | A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks |
| title_full | A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks |
| title_fullStr | A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks |
| title_full_unstemmed | A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks |
| title_short | A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks |
| title_sort | methodology for evaluating algorithms that calculate social influence in complex social networks |
| url | http://dx.doi.org/10.1155/2018/1084795 |
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