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A graph grammar and K4-type tournament-based approach to detect conflicts of interest in a social network.

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Title: A graph grammar and K4-type tournament-based approach to detect conflicts of interest in a social network.
Authors: Albane, Saadia1 (AUTHOR) saadialbane@gmail.com, Slimani, Hachem1 (AUTHOR), Kheddouci, Hamamache2 (AUTHOR)
Source: Knowledge & Information Systems. 2021, Vol. 63 Issue 2, p497-539. 43p.
Subjects: Graph grammars, Social conflict, Social networks, Conflict of interests, Conferences & conventions
Abstract: In this paper, we introduce a new approach based on properties of graph grammars to detect conflicts of interest (COIs) in a field represented in the form of a social network. The approach consists of specializing the adaptive star graph grammar (ASGG) of Drewes et al. (Theor Comput Sci 411:3090–3109, 2010) to express kind of subgraphs that we call K 4 -type tournament graphs, corresponding to COIs, that cannot be generated by the node replacement graph grammar. This approach, called graph grammar and K 4 -type tournament-based approach to detect conflicts of interest (G G K 4 T - C O I s) , is applied to detect COIs in the review process of papers accepted in an international conference which is represented through a social network. In this contribution, the principle of the used graph grammar is not to consider all the generated language but only subgraphs with some properties (corresponding to special graph queries), which identify parts of the social network representing COIs. For evaluating the performances and the efficiency of our proposition, experimentations have been done by comparing it with concurrent methods in the literature. The obtained results have shown that the approach GG K 4 T-COIs performs better than the investigated state-of-the-art approaches in terms of type and number of detected COIs. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A graph grammar and K4-type tournament-based approach to detect conflicts of interest in a social network.
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  Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. 2021, Vol. 63 Issue 2, p497-539. 43p.
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  Data: <searchLink fieldCode="DE" term="%22Graph+grammars%22">Graph grammars</searchLink><br /><searchLink fieldCode="DE" term="%22Social+conflict%22">Social conflict</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Conflict+of+interests%22">Conflict of interests</searchLink><br /><searchLink fieldCode="DE" term="%22Conferences+%26+conventions%22">Conferences & conventions</searchLink>
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  Data: In this paper, we introduce a new approach based on properties of graph grammars to detect conflicts of interest (COIs) in a field represented in the form of a social network. The approach consists of specializing the adaptive star graph grammar (ASGG) of Drewes et al. (Theor Comput Sci 411:3090–3109, 2010) to express kind of subgraphs that we call K 4 -type tournament graphs, corresponding to COIs, that cannot be generated by the node replacement graph grammar. This approach, called graph grammar and K 4 -type tournament-based approach to detect conflicts of interest (G G K 4 T - C O I s) , is applied to detect COIs in the review process of papers accepted in an international conference which is represented through a social network. In this contribution, the principle of the used graph grammar is not to consider all the generated language but only subgraphs with some properties (corresponding to special graph queries), which identify parts of the social network representing COIs. For evaluating the performances and the efficiency of our proposition, experimentations have been done by comparing it with concurrent methods in the literature. The obtained results have shown that the approach GG K 4 T-COIs performs better than the investigated state-of-the-art approaches in terms of type and number of detected COIs. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10115-020-01525-5
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        Text: English
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        Type: general
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      – SubjectFull: Social networks
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      – SubjectFull: Conflict of interests
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              Text: 2021
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