UPPR+: Scaling Uncertain Personalised PageRank Computation on Billion-Sized Graphs with Mutually Exclusive Edges
Year: 2025
Type: conference-paper
Abstract: While Personalised PageRank (PPR) is widely used for ranking nodes in certain graphs, research on PPR for uncertain graphs remains limited. Real-world graphs often exhibit uncertainty in some edges with interdependent probabilities. The best-of-breed work by Kim et al.[13] proposed a fast approximate algorithm, UPPR, leveraging the Sherman-Morrison formula with singular value decomposition. However, UPPR lacks error guarantees, and struggles to scale on large graphs due to the high cost to precompute block matrix inverses over the certain part of the graph.
Authors: Min Zhang, Weiren Yu
Institution: University of Warwick
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412378019