Information Source Finding in Networks: Querying with Budgets

TL;DR

This paper investigates the problem of identifying the source of information diffusion in networks through querying, considering costs, potential respondent dishonesty, and adaptive versus non-adaptive strategies, providing theoretical bounds and practical algorithms.

Contribution

It introduces a comprehensive analysis of source detection with querying budgets, proposing algorithms and quantifying the adaptivity gap in information source identification.

Findings

Derived information-theoretic lower bounds for query budgets.

Proposed practical adaptive and non-adaptive estimation algorithms.

Quantified the extra budget needed for non-adaptive algorithms.

Abstract

In this paper, we study a problem of detecting the source of diffused information by querying individuals, given a sample snapshot of the information diffusion graph, where two queries are asked: {\em (i)} whether the respondent is the source or not, and {\em (ii)} if not, which neighbor spreads the information to the respondent. We consider the case when respondents may not always be truthful and some cost is taken for each query. Our goal is to quantify the necessary and sufficient budgets to achieve the detection probability $1-\delta$ for any given $0<\delta<1.$ To this end, we study two types of algorithms: adaptive and non-adaptive ones, each of which corresponds to whether we adaptively select the next respondents based on the answers of the previous respondents or not. We first provide the information theoretic lower bounds for the necessary budgets in both algorithm types. In terms of the sufficient budgets, we propose two practical estimation algorithms, each of non-adaptive and adaptive types, and for each algorithm, we quantitatively analyze the budget which ensures $1-\delta$ detection accuracy. This theoretical analysis not only quantifies the budgets needed by practical estimation algorithms achieving a given target detection accuracy in finding the diffusion source, but also enables us to quantitatively characterize the amount of extra budget required in non-adaptive type of estimation, refereed to as {\em adaptivity gap}. We validate our theoretical findings over synthetic and real-world social network topologies.