Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach
The emerging social network platforms enable users to share their own opinions, as well as to exchange opinions with others. However, adversarial network perturbation, where malicious users intentionally spread their extreme opinions, rumors, and misinformation to others, is ubiquitous in social net...
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creator | Li, Yuejiang Chen, Zhanjiang Zhao, H. Vicky |
description | The emerging social network platforms enable users to share their own
opinions, as well as to exchange opinions with others. However, adversarial
network perturbation, where malicious users intentionally spread their extreme
opinions, rumors, and misinformation to others, is ubiquitous in social
networks. Such adversarial network perturbation greatly influences the opinion
formation of the public and threatens our societies. Thus, it is critical to
study and control the influence of adversarial network perturbation. Although
tremendous efforts have been made in both academia and industry to guide and
control the public opinion dynamics, most of these works assume that the
network is static, and ignore such adversarial network perturbation. In this
work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we
model the adversarial network perturbation and analyze its impact on the
networks' opinion. Then, from the adversary's perspective, we analyze its
optimal network perturbation, which maximally changes the network's opinion.
Next, from the network defender's perspective, we formulate a Stackelberg game
and aim to control the network's opinion even under such adversarial network
perturbation. We devise a projected subgradient algorithm to solve the
formulated Stackelberg game. Extensive simulations on real social networks
validate our analysis of the adversarial network perturbation's influence and
the effectiveness of the proposed opinion control algorithm. |
doi_str_mv | 10.48550/arxiv.2304.12540 |
format | Article |
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opinions, as well as to exchange opinions with others. However, adversarial
network perturbation, where malicious users intentionally spread their extreme
opinions, rumors, and misinformation to others, is ubiquitous in social
networks. Such adversarial network perturbation greatly influences the opinion
formation of the public and threatens our societies. Thus, it is critical to
study and control the influence of adversarial network perturbation. Although
tremendous efforts have been made in both academia and industry to guide and
control the public opinion dynamics, most of these works assume that the
network is static, and ignore such adversarial network perturbation. In this
work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we
model the adversarial network perturbation and analyze its impact on the
networks' opinion. Then, from the adversary's perspective, we analyze its
optimal network perturbation, which maximally changes the network's opinion.
Next, from the network defender's perspective, we formulate a Stackelberg game
and aim to control the network's opinion even under such adversarial network
perturbation. We devise a projected subgradient algorithm to solve the
formulated Stackelberg game. Extensive simulations on real social networks
validate our analysis of the adversarial network perturbation's influence and
the effectiveness of the proposed opinion control algorithm.</description><identifier>DOI: 10.48550/arxiv.2304.12540</identifier><language>eng</language><subject>Computer Science - Computers and Society</subject><creationdate>2023-04</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2304.12540$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2304.12540$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Yuejiang</creatorcontrib><creatorcontrib>Chen, Zhanjiang</creatorcontrib><creatorcontrib>Zhao, H. Vicky</creatorcontrib><title>Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach</title><description>The emerging social network platforms enable users to share their own
opinions, as well as to exchange opinions with others. However, adversarial
network perturbation, where malicious users intentionally spread their extreme
opinions, rumors, and misinformation to others, is ubiquitous in social
networks. Such adversarial network perturbation greatly influences the opinion
formation of the public and threatens our societies. Thus, it is critical to
study and control the influence of adversarial network perturbation. Although
tremendous efforts have been made in both academia and industry to guide and
control the public opinion dynamics, most of these works assume that the
network is static, and ignore such adversarial network perturbation. In this
work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we
model the adversarial network perturbation and analyze its impact on the
networks' opinion. Then, from the adversary's perspective, we analyze its
optimal network perturbation, which maximally changes the network's opinion.
Next, from the network defender's perspective, we formulate a Stackelberg game
and aim to control the network's opinion even under such adversarial network
perturbation. We devise a projected subgradient algorithm to solve the
formulated Stackelberg game. Extensive simulations on real social networks
validate our analysis of the adversarial network perturbation's influence and
the effectiveness of the proposed opinion control algorithm.</description><subject>Computer Science - Computers and Society</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAUhmEvDKhwAUz4BhL8l8RmiyIoSBVFavfo2DkGq2kSnaYF7h4oTN_y6pMexm6kyI0tCnEH9JlOudLC5FIVRlyyzXpKQxoH3ozDTGPPj0OHxOvuhHQAStDzF5w_RtrxV6T5SB7mn_ye13wzQ9hh75He-BL2yOtpohHC-xW7iNAf8Pp_F2z7-LBtnrLVevnc1KsMykpkEGNQwnWxw-jARlmV1krrtBfRKCeD0qUshdcofJDGViqI4ByWtghSVVov2O3f7VnVTpT2QF_tr6496_Q3YN9KSw</recordid><startdate>20230424</startdate><enddate>20230424</enddate><creator>Li, Yuejiang</creator><creator>Chen, Zhanjiang</creator><creator>Zhao, H. Vicky</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230424</creationdate><title>Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach</title><author>Li, Yuejiang ; Chen, Zhanjiang ; Zhao, H. Vicky</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-affc209dfdef9a8f176881893b0f4291c236160b3e0bc14872c0c99e685c12733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computers and Society</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Yuejiang</creatorcontrib><creatorcontrib>Chen, Zhanjiang</creatorcontrib><creatorcontrib>Zhao, H. Vicky</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Yuejiang</au><au>Chen, Zhanjiang</au><au>Zhao, H. Vicky</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach</atitle><date>2023-04-24</date><risdate>2023</risdate><abstract>The emerging social network platforms enable users to share their own
opinions, as well as to exchange opinions with others. However, adversarial
network perturbation, where malicious users intentionally spread their extreme
opinions, rumors, and misinformation to others, is ubiquitous in social
networks. Such adversarial network perturbation greatly influences the opinion
formation of the public and threatens our societies. Thus, it is critical to
study and control the influence of adversarial network perturbation. Although
tremendous efforts have been made in both academia and industry to guide and
control the public opinion dynamics, most of these works assume that the
network is static, and ignore such adversarial network perturbation. In this
work, based on the well-accepted Friedkin-Johnsen opinion dynamics model, we
model the adversarial network perturbation and analyze its impact on the
networks' opinion. Then, from the adversary's perspective, we analyze its
optimal network perturbation, which maximally changes the network's opinion.
Next, from the network defender's perspective, we formulate a Stackelberg game
and aim to control the network's opinion even under such adversarial network
perturbation. We devise a projected subgradient algorithm to solve the
formulated Stackelberg game. Extensive simulations on real social networks
validate our analysis of the adversarial network perturbation's influence and
the effectiveness of the proposed opinion control algorithm.</abstract><doi>10.48550/arxiv.2304.12540</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computers and Society |
title | Opinion Control under Adversarial Network Perturbation: A Stackelberg Game Approach |
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