Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks
Viral marketing, a marketing strategy that utilizes word-of-mouth (WOM), is effective in increasing brand awareness and acquiring new customers, as WOM allows information to reach a large audience in social networks. In the past two decades, for efficient viral marketing, many studies on maximizing...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816425/ |
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| author | Satoshi Furutani Tatsuhiro Aoshima Toshiki Shibahara Mitsuaki Akiyama Masaki Aida |
| author_facet | Satoshi Furutani Tatsuhiro Aoshima Toshiki Shibahara Mitsuaki Akiyama Masaki Aida |
| author_sort | Satoshi Furutani |
| collection | DOAJ |
| description | Viral marketing, a marketing strategy that utilizes word-of-mouth (WOM), is effective in increasing brand awareness and acquiring new customers, as WOM allows information to reach a large audience in social networks. In the past two decades, for efficient viral marketing, many studies on maximizing advertising reach, known as influence maximization, have been conducted in the field of data mining. However, most of them ignore the possibility of the emergence of negative opinions in the information diffusion process. In general, negative opinions are more contagious than positive ones, and ignoring them may even lead undesirable outcomes, such as a decline in brand image and a decrease in purchases. To address this issue, we consider the problem of suppressing the negative influence that emerges endogenously on social networks through preemptive node interventions, such as persuasion, nudging, or warnings. Namely, given a limited budget for interventions, who should be targeted to efficiently suppress the spread of negative opinions in the social network? We formulate this problem as a combinatorial optimization problem on graphs. We prove that this problem is NP-hard and propose approximation algorithms to identify optimal intervention nodes that minimize the negative influence. Through numerical experiments, we demonstrate that our algorithms effectively suppress the negative influence regardless of the type of social network or experimental setting. |
| format | Article |
| id | doaj-art-07d934125aa548a6ac40785d4769a43b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-07d934125aa548a6ac40785d4769a43b2025-01-21T00:01:29ZengIEEEIEEE Access2169-35362025-01-01139290930210.1109/ACCESS.2024.352303610816425Suppressing the Endogenous Negative Influence Through Node Intervention in Social NetworksSatoshi Furutani0https://orcid.org/0000-0003-0565-8912Tatsuhiro Aoshima1Toshiki Shibahara2Mitsuaki Akiyama3https://orcid.org/0000-0001-7052-8562Masaki Aida4https://orcid.org/0000-0001-5614-6269NTT Social Informatics Laboratories, Musashino, Tokyo, JapanNTT Social Informatics Laboratories, Musashino, Tokyo, JapanNTT Social Informatics Laboratories, Musashino, Tokyo, JapanNTT Social Informatics Laboratories, Musashino, Tokyo, JapanGraduate School of Systems Design, Tokyo Metropolitan University, Hino, Tokyo, JapanViral marketing, a marketing strategy that utilizes word-of-mouth (WOM), is effective in increasing brand awareness and acquiring new customers, as WOM allows information to reach a large audience in social networks. In the past two decades, for efficient viral marketing, many studies on maximizing advertising reach, known as influence maximization, have been conducted in the field of data mining. However, most of them ignore the possibility of the emergence of negative opinions in the information diffusion process. In general, negative opinions are more contagious than positive ones, and ignoring them may even lead undesirable outcomes, such as a decline in brand image and a decrease in purchases. To address this issue, we consider the problem of suppressing the negative influence that emerges endogenously on social networks through preemptive node interventions, such as persuasion, nudging, or warnings. Namely, given a limited budget for interventions, who should be targeted to efficiently suppress the spread of negative opinions in the social network? We formulate this problem as a combinatorial optimization problem on graphs. We prove that this problem is NP-hard and propose approximation algorithms to identify optimal intervention nodes that minimize the negative influence. Through numerical experiments, we demonstrate that our algorithms effectively suppress the negative influence regardless of the type of social network or experimental setting.https://ieeexplore.ieee.org/document/10816425/Influence maximizationinformation diffusionsocial networks |
| spellingShingle | Satoshi Furutani Tatsuhiro Aoshima Toshiki Shibahara Mitsuaki Akiyama Masaki Aida Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks IEEE Access Influence maximization information diffusion social networks |
| title | Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks |
| title_full | Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks |
| title_fullStr | Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks |
| title_full_unstemmed | Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks |
| title_short | Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks |
| title_sort | suppressing the endogenous negative influence through node intervention in social networks |
| topic | Influence maximization information diffusion social networks |
| url | https://ieeexplore.ieee.org/document/10816425/ |
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