# Majority Opinion Diffusion in Social Networks: An Adversarial Approach

arXiv: https://arxiv.org/abs/2012.03143 · HTML: https://mlsanity.com/paper/2012.03143/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 5 Dec 2020.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 12 of 12)

- [Concentration and regularization of random graphs](https://mlsanity.com/paper/1506.00669/) (2015)
- [Controlling Elections through Social Influence](https://mlsanity.com/paper/1711.08615/) (2017)
- [Convergence of Opinion Diffusion is PSPACE-complete](https://mlsanity.com/paper/1912.09864/) (2019)
- [Biased Opinion Dynamics: When the Devil Is in the Details](https://mlsanity.com/paper/2008.13589/) (2020)
- [Opinion Forming in Erdos-Renyi Random Graph and Expanders](https://mlsanity.com/paper/1805.12172/) (2018)
- [Opinion Diffusion and Campaigning on Society Graphs](https://mlsanity.com/paper/2010.00651/) (2020)
- [Majority Model on Random Regular Graphs](https://mlsanity.com/paper/1711.07423/) (2017)
- [Approximation Algorithm for the Partial Set Multi-Cover Problem](https://mlsanity.com/paper/1811.08185/) (2018)
- [On the Voting Time of the Deterministic Majority Process](https://mlsanity.com/paper/1508.03519/) (2015)
- [Agent-based opinion formation modeling in social network: a perspective of social psychology](https://mlsanity.com/paper/1807.06182/) (2018)
- [Tight Bounds on the Minimum Size of a Dynamic Monopoly](https://mlsanity.com/paper/1901.05917/) (2018)
- [Spread of Influence in Graphs](https://mlsanity.com/paper/2006.03440/) (2020)

## Built on by (most cited first, 4 of 4)

- [Random Majority Opinion Diffusion: Stabilization Time, Absorbing States, and Influential Nodes](https://mlsanity.com/paper/2302.06760/) (2023)
- [Models we Can Trust: Toward a Systematic Discipline of (Agent-Based) Model Interpretation and Validation](https://mlsanity.com/paper/2102.11615/) (2021)
- [Viral Marketing in Social Networks with Competing Products](https://mlsanity.com/paper/2312.15819/) (2023)
- [A Generalisation of Voter Model: Influential Nodes and Convergence Properties](https://mlsanity.com/paper/2411.04564/) (2024)

## Abstract (arXiv)

We introduce and study a novel majority-based opinion diffusion model. Consider a graph G, which represents a social network. Assume that initially a subset of nodes, called seed nodes or early adopters, are colored either black or white, which correspond to positive or negative opinion regarding a consumer product or a technological innovation. Then, in each round an uncolored node, which is adjacent to at least one colored node, chooses the most frequent color among its neighbors. Consider a marketing campaign which advertises a product of poor quality and its ultimate goal is that more than half of the population believe in the quality of the product at the end of the opinion diffusion process. We focus on three types of attackers which can select the seed nodes in a deterministic or random fashion and manipulate almost half of them to adopt a positive opinion toward the product (that is, to choose black color). We say that an attacker succeeds if a majority of nodes are black at the end of the process. Our main purpose is to characterize classes of graphs where an attacker cannot succeed. In particular, we prove that if the maximum degree of the underlying graph is not too large or if it has strong expansion properties, then it is fairly resilient to such attacks. Furthermore, we prove tight bounds on the stabilization time of the process (that is, the number of rounds it needs to end) in both settings of choosing the seed nodes deterministically and randomly. We also provide several hardness results for some optimization problems regarding stabilization time and choice of seed nodes.