Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

TL;DR

This paper introduces a visual analytics framework that helps developers and analysts explore how small changes in graph structures affect the rankings produced by algorithms like PageRank and HITS, enhancing understanding of their sensitivities.

Contribution

The paper presents a novel visual analytics tool for analyzing the sensitivity of graph-based ranking algorithms through perturbation-based what-if analysis.

Findings

Framework effectively reveals ranking sensitivities

Case studies demonstrate practical insights into algorithm behavior

Supports exploration of perturbations in real-world networks

Abstract

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.