CycleRank, or there and back again: personalized relevance scores from cyclic paths on directed graphs
Cristian Consonni1, David Laniado1, Alberto Montresor2
1Eurecat - Centre Tecnògic of Catalunya, Barcelona, Spain.
Proceedings. Mathematical, Physical, and Engineering Sciences
|October 19, 2020
Summary
CycleRank, a new algorithm using cyclic paths, improves Wikipedia article relevance ranking. It better reflects user behavior and is significantly faster than Personalized PageRank.
Area of Science:
- Information Science
- Computer Science
- Network Analysis
Background:
- Wikipedia link surfing aids knowledge discovery.
- Personalized PageRank identifies relevant articles but is affected by high-indegree hubs.
Purpose of the Study:
- Introduce CycleRank, a novel algorithm for identifying relevant Wikipedia articles.
- Evaluate CycleRank's performance against Personalized PageRank and related algorithms.
Main Methods:
- Developed CycleRank algorithm based on cyclic paths.
- Conducted three experiments using different ground truths for comparison.
- Assessed algorithm performance based on reader click behavior and editor-highlighted links.
Main Results:
- CycleRank aligns better with user click behavior.
- CycleRank identifies articles from 'See also' sections more effectively.
- CycleRank is more robust to high-indegree hubs and significantly faster (two orders of magnitude) than baselines.
Conclusions:
- CycleRank offers a more accurate and efficient method for determining article relevance on Wikipedia.
- The algorithm's speed and alignment with user behavior make it a valuable tool for knowledge discovery.
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