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AURORA: Auditing PageRank on Large Graphs by Kang, Jian; Wang, Meijia; Cao, Nan; Xia, Yinglong; Fan, Wei; Tong, Hanghang is a scholarly article available to read on EtoBox.

What is AURORA: Auditing PageRank on Large Graphs about?

Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, biology to neuroscience and many more. PageRank, together with many of its random walk based variants, has become one of the most well-known and widely used algorithms, due to its mathematical elegance and the superior performance across a variety of application domains. Important as it might be, state-of-the-art lacks an intuitive way to explain the ranking results by PageRank (or its variants), e.g., why it thinks the returned top-k webpages are most important ones in the entire graph; why it gives a higher rank to actor John than actor Smith in terms of their relevance w.r.t. a particular movie? In order to answer these questions, this paper proposes a paradigm shift for PageRank, from identifying which nodes are most important to understanding why the ranking algorithm gives a particular ranking result. We formally define the PageRank auditing problem, whose central idea is to identify a set of key graph elements (e.g., edges, nodes, subgraphs) with the highest influence on the ranking results. We formul

Author

Kang, Jian; Wang, Meijia; Cao, Nan; Xia, Yinglong; Fan, Wei; Tong, Hanghang

Published

2018

Language

EN