Delusive PageRank in Incomplete Graphs

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


Most real-world graphs collected from the Web like Web graphs and social network graphs are incomplete. This leads to inaccurate estimates of graph properties based on link analysis such as PageRank. In this paper we focus on studying such deviations in ordering/ranking imposed by PageRank over incomplete graphs. We first show that deviations in rankings induced by PageRank are indeed possible. We measure how much a ranking, induced by PageRank, on an input graph could deviate from the original unseen graph. More importantly, we are interested in conceiving a measure that approximates the rank correlation among them without any knowledge of the original graph. To this extent we formulate the HAK measure that is based on computing the impact redistribution of PageRank according to the local graph structure. Finally, we perform extensive experiments on both real-world Web and social network graphs with more than 100M vertices and 10B edges as well as synthetic graphs to showcase the utility of HAK.


Incomplete Graph Real-world Graphs Synthetic Graphs Social Network Graph Ghost Vertex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is partially funded by ALEXANDRIA (ERC 339233).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz UniversityHannoverGermany
  2. 2.KBSLeibniz UniversityHannoverGermany

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