Fast personalized PageRank on MapReduce
By Bahman Bahmani, Kaushik Chakrabarti, Dong Xin. 2011
162 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stephan Günnemann
- Aleksandar Bojchevski
- Johannes Klicpera
- Andrea Vattani
- Michael J. Kane
- Peter Bhlmann
- Petros Drineas
- Ravi Kumar
In Computer Vision and Pattern Recognition
- Andrea Vattani
- Johannes Klicpera
- Ravi Kumar
- Stephan Günnemann
- Benjamin Moseley
- Bahman Bahmani
- Aneesh Sharma
- Peter Bhlmann
In Signal Processing
- Sergei Vassilvitskii
- Jimmy J. Lin
- Peter Bhlmann
- M. J. van der Laan
- Michael J. Kane
- Benjamin Moseley
- Surajit Chaudhuri
- Ben Y. Zhao
Citations in the corpus
- Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, background): “Furthermore, faster or incremental approximations of personalized PageRank (Bahmani et al., 2010; 2011; Lofgren et al., 2014) and more sophisticated propagation schemes would also benefit the method.”
- Scalable K-Means++ (2012, background): “Since the pioneering work by Dean and Ghemawat [13], MapReduce and its open source version, Hadoop [33], has become a de facto standard for large data analysis, and a variety of algorithms have been designed for it [7, 6].”
- Handbook of Big Data (2016, methodology): “After each brushing operation, the lens scatterplot will follow suit and color its points to match those in the main window.”
- On the Embeddability of Random Walk Distances (2013, methodology): “Monte Carlo algorithms to compute PPR can be significantly sped up using a variety of techniques, ranging from parallelization via MapReduce [4] to improved bounds for distributed algorithms [11].”
- InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “We use the recently proposed MapReduce algorithm to compute the FPPR [1].”
- GraphJet: Real-Time Content Recommendations at Twitter (2016, methodology): “To introduce personalization, we include a reset step on the left-to-right edge traversals: with a fixed probability α , we restart from the query vertex to ensure that the random walk doesn’t “stray” too far from the query vertex, which is the same intuition behind personalized PageRank [13, 4].”