Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

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.

arXiv comment11 pages, accepted by IEEE Transactions on Visualization and Computer Graphics

References in corpus (10)

• Adversarial Attacks on Neural Networks for Graph Data
• Fairness of Exposure in Rankings
• The What-If Tool: Interactive Probing of Machine Learning Models
• Adversarial Attack on Graph Structured Data
• Fast Gradient Attack on Network Embedding
• What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
• A Deep Generative Model for Graph Layout
• Attack Graph Convolutional Networks by Adding Fake Nodes
• Data Poisoning Attack against Unsupervised Node Embedding Methods
• Link Prediction Adversarial Attack