Estimating Node Influenceability in Social Networks

TL;DR

This paper introduces new stratified sampling estimators to accurately evaluate node influenceability in social networks under the independent cascade model, reducing variance compared to traditional Monte Carlo methods.

Contribution

It proposes four novel stratified sampling estimators that are unbiased and have lower variance for influenceability evaluation in social networks.

Findings

Estimators significantly reduce variance compared to Naive Monte Carlo.

Experimental results show improved efficiency and accuracy on synthetic and real datasets.

Proposed methods outperform existing influence estimation techniques.

Abstract

Influence analysis is a fundamental problem in social network analysis and mining. The important applications of the influence analysis in social network include influence maximization for viral marketing, finding the most influential nodes, online advertising, etc. For many of these applications, it is crucial to evaluate the influenceability of a node. In this paper, we study the problem of evaluating influenceability of nodes in social network based on the widely used influence spread model, namely, the independent cascade model. Since this problem is #P-complete, most existing work is based on Naive Monte-Carlo (\nmc) sampling. However, the \nmc estimator typically results in a large variance, which significantly reduces its effectiveness. To overcome this problem, we propose two families of new estimators based on the idea of stratified sampling. We first present two basic stratified sampling (\bss) estimators, namely \bssi estimator and \bssii estimator, which partition the entire population into $2^r$ and $r+1$ strata by choosing $r$ edges respectively. Second, to further reduce the variance, we find that both \bssi and \bssii estimators can be recursively performed on each stratum, thus we propose two recursive stratified sampling (\rss) estimators, namely \rssi estimator and \rssii estimator. Theoretically, all of our estimators are shown to be unbiased and their variances are significantly smaller than the variance of the \nmc estimator. Finally, our extensive experimental results on both synthetic and real datasets demonstrate the efficiency and accuracy of our new estimators.