Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

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...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 27; číslo 2; s. 1459 - 1469
Hlavní autoři: Xie, Tiankai, Ma, Yuxin, Tong, Hanghang, Thai, My T., Maciejewski, Ross
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Blogs
Graph-based ranking
Hypertext systems
Information retrieval
Layout
News media
Perturbation
Perturbation methods
Questions
Ranking
Search engines
Sensitivity
Sensitivity analysis
Social networks
Task analysis
Visual analytics
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí: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.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2020.3028958