Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Gunagmo Tong, Ding-Zhu Du

TL;DR

A novel sampling method is proposed which is able to attach high weights to the users who are prone to be affected by the misinformation and is more powerful in generating effective samples used for computing seed nodes for the positive cascade.

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 $(1-1/e-ε)$-approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.

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

TL;DR

A novel sampling method is proposed which is able to attach high weights to the users who are prone to be affected by the misinformation and is more powerful in generating effective samples used for computing seed nodes for the positive cascade.

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 -approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.

Paper Structure

This paper contains 3 sections, 1 theorem, 5 equations, 2 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Derandomization of the diffusion process

Key Result

Lemma 1

Suppose the seed set of the positive cascade is $S \subseteq V$. For each $v \in V$ and $g \in \mathop{\mathrm{\mathcal{G}}}\nolimits$, $v$ is $\mathop{\mathrm{\overline{C}}}\nolimits_r$-active in $g$ if and only if $\mathop{\mathrm{dis}}\nolimits_g(S, v) < \mathop{\mathrm{dis}}\nolimits_g(S_r,v)$ o

Figures (2)

  • Figure 1: An illustrative example
  • Figure 2: Sample patterns

Theorems & Definitions (2)

  • Definition 1: Realization
  • Lemma 1