[Paper Review] Scalable Misinformation Prevention in Social Networks.

Michael Simpson, Venkatesh Srinivasan+1 more|arXiv (Cornell University)|Jul 1, 2018

Complexity and Algorithms in Graphs9 references4 citations

TL;DR

This paper proposes RPS (Reverse Prevention Sampling), a scalable algorithm for preventing misinformation in social networks by selecting k users to adopt a 'limiting' campaign that counters a bad campaign. RPS achieves a (1 - 1/e - ε)-approximate solution in O((k + l)(n + m)(1/(1 - γ)) log n / ε²) expected time, significantly outperforming prior methods in speed while maintaining strong theoretical guarantees.

ABSTRACT

In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a bad campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or good) campaign to counteract the effect of misinformation. The goal is then to identify a subset of $k$ users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the bad campaign at the end of both propagation processes. This work presents \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation prevention problem. Our theoretical analysis shows that \emph{RPS} runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2)$ expected time and returns a $(1 - 1/e - \epsilon)$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma$ is a typically small network parameter). The time complexity of \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. We experimentally evaluate \emph{RPS} on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation prevention can be made practical while still offering strong theoretical guarantees.

Motivation & Objective

• To address the challenge of preventing the spread of misinformation in large-scale social networks through strategic intervention.
• To identify a subset of k users who, when convinced to adopt a 'limiting' (or good) campaign, minimize the final adoption of a malicious (bad) campaign.
• To develop a scalable solution that maintains high-quality approximation guarantees despite the computational complexity of the problem.
• To improve upon the runtime of existing algorithms, which scale poorly with network size and parameter precision.

Proposed method

• RPS employs a reverse sampling technique to efficiently identify high-impact users for intervention without exhaustively evaluating all possible k-user subsets.
• The algorithm leverages a probabilistic framework to ensure a (1 - 1/e - ε)-approximate solution with high probability (at least 1 - n^(-l)).
• It uses a network parameter γ, representing the typical decay in influence propagation, to bound the expected runtime and influence spread.
• RPS reduces the time complexity from Ω(m n k · POLY(ε^(-1))) in prior methods to O((k + l)(n + m)(1/(1 - γ)) log n / ε²), enabling scalability.
• The method relies on iterative sampling of reverse reachable sets to estimate the influence of candidate users, focusing on high-impact nodes.
• Theoretical analysis combines concentration inequalities and influence spread modeling to bound approximation error and failure probability.

Experimental results

Research questions

• RQ1 Can we design a scalable algorithm that provides strong theoretical approximation guarantees for misinformation prevention in large social networks?
• RQ2 How can we reduce the runtime of influence maximization-style algorithms for misinformation prevention without sacrificing solution quality?
• RQ3 What is the impact of network structure and influence decay (parameter γ) on the efficiency and accuracy of prevention strategies?
• RQ4 Can reverse sampling techniques be effectively adapted to the misinformation prevention problem to achieve both speed and accuracy?

Key findings

• RPS achieves a (1 - 1/e - ε)-approximate solution with at least 1 - n^(-l) probability, matching the theoretical upper bound of the influence maximization problem.
• The expected runtime of RPS is O((k + l)(n + m)(1/(1 - γ)) log n / ε²), which is a substantial improvement over prior algorithms with Ω(m n k · POLY(ε^(-1))) complexity.
• On large-scale datasets, RPS outperforms the state-of-the-art solution by several orders of magnitude in running time while maintaining high solution quality.
• The algorithm's performance is robust across diverse network structures, with runtime scaling favorably with network size and influence decay parameter γ.
• The use of reverse sampling enables efficient estimation of influence spread, making large-scale deployment of misinformation prevention feasible.
• Theoretical guarantees are preserved even under probabilistic assumptions, ensuring reliability in real-world network conditions.