Opinion Control under Adversarial Network Perturbation: A Stackelberg
Game Approach
- URL: http://arxiv.org/abs/2304.12540v1
- Date: Tue, 25 Apr 2023 03:14:39 GMT
- Title: Opinion Control under Adversarial Network Perturbation: A Stackelberg
Game Approach
- Authors: Yuejiang Li, Zhanjiang Chen, H. Vicky Zhao
- Abstract summary: adversarial network perturbation greatly influences the opinion formation of the public and threatens our societies.
In this work, we model the adversarial network perturbation and analyze its impact on the networks' opinion.
From the adversary's perspective, we formulate a Stackelberg game and aim to control the network's opinion even under such adversarial network perturbation.
- Score: 12.916992671437017
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor [6.089685202183291]
Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains.
Previous works pretrain the teacher network to make it robust against the adversarial examples aimed at itself.
We propose PeerAiD to make a peer network learn the adversarial examples of the student network instead of adversarial examples aimed at itself.
arXiv Detail & Related papers (2024-03-11T12:36:14Z) - Emergent Influence Networks in Good-Faith Online Discussions [3.678291991600161]
This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates.
We propose a novel framework for measuring the impact of network position on persuasiveness using a combination of social network analysis and machine learning.
arXiv Detail & Related papers (2023-06-23T00:24:50Z) - Stimulative Training++: Go Beyond The Performance Limits of Residual
Networks [91.5381301894899]
Residual networks have shown great success and become indispensable in recent deep neural network models.
Previous research has suggested that residual networks can be considered as ensembles of shallow networks.
We identify a problem that is analogous to social loafing, whereworks within a residual network are prone to exert less effort when working as part of a group compared to working alone.
arXiv Detail & Related papers (2023-05-04T02:38:11Z) - Stimulative Training of Residual Networks: A Social Psychology
Perspective of Loafing [86.69698062642055]
Residual networks have shown great success and become indispensable in today's deep models.
We aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing.
We propose a new training strategy to strengthen the performance of residual networks.
arXiv Detail & Related papers (2022-10-09T03:15:51Z) - Robustness and stability of enterprise intranet social networks: The
impact of moderators [0.0]
We analyzed more than 52,000 messages posted by approximately 12,000 employees.
We removed the forum moderators, the spammers, the overly connected nodes and the nodes lying at the network periphery.
Our findings can help online community managers to understand the role of moderators within intranet forums.
arXiv Detail & Related papers (2021-05-19T13:43:03Z) - NetReAct: Interactive Learning for Network Summarization [60.18513812680714]
We present NetReAct, a novel interactive network summarization algorithm which supports the visualization of networks induced by text corpora to perform sensemaking.
We show how NetReAct is successful in generating high-quality summaries and visualizations that reveal hidden patterns better than other non-trivial baselines.
arXiv Detail & Related papers (2020-12-22T03:56:26Z) - A Survey on Computational Propaganda Detection [31.42480765785039]
Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda.
They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts.
arXiv Detail & Related papers (2020-07-15T22:25:51Z) - Proper Network Interpretability Helps Adversarial Robustness in
Classification [91.39031895064223]
We show that with a proper measurement of interpretation, it is difficult to prevent prediction-evasion adversarial attacks from causing interpretation discrepancy.
We develop an interpretability-aware defensive scheme built only on promoting robust interpretation.
We show that our defense achieves both robust classification and robust interpretation, outperforming state-of-the-art adversarial training methods against attacks of large perturbation.
arXiv Detail & Related papers (2020-06-26T01:31:31Z) - Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness [97.67477497115163]
We use mode connectivity to study the adversarial robustness of deep neural networks.
Our experiments cover various types of adversarial attacks applied to different network architectures and datasets.
Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness.
arXiv Detail & Related papers (2020-04-30T19:12:50Z) - DiffNet++: A Neural Influence and Interest Diffusion Network for Social
Recommendation [50.08581302050378]
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences.
We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet)
In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
arXiv Detail & Related papers (2020-01-15T08:45:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.