Beyond Uniform Reverse Sampling: A Hybrid Sampling Technique for Misinformation Prevention

TL;DR

This paper introduces a hybrid sampling technique for misinformation prevention that focuses on influential nodes, leading to more effective seed selection and improved prevention strategies under the independent cascade model.

Contribution

It proposes a novel hybrid sampling method that assigns higher weights to susceptible users, enhancing influence estimation and seed selection for misinformation prevention.

Findings

The new sampling method outperforms existing techniques in experiments.

The algorithm achieves a near-optimal approximation ratio.

Experimental results show significant improvements over state-of-the-art solutions.

Abstract

Online misinformation has been considered as one of the top global risks as it may cause serious consequences such as economic damages and public panic. The misinformation prevention problem aims at generating a positive cascade with appropriate seed nodes in order to compete against the misinformation. In this paper, we study the misinformation prevention problem under the prominent independent cascade model. Due to the #P-hardness in computing influence, the core problem is to design effective sampling methods to estimate the function value. The main contribution of this paper is a novel sampling method. Different from the classic reverse sampling technique which treats all nodes equally and samples the node uniformly, the proposed method proceeds with a hybrid sampling process which is able to attach high weights to the users who are prone to be affected by the misinformation. Consequently, the new sampling method is more powerful in generating effective samples used for computing seed nodes for the positive cascade. Based on the new hybrid sample technique, we design an algorithm offering a $(1-1/e-\epsilon)$-approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.