The PageRank Citation Ranking : Bringing Order to the Web

By Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. 1999

Subfields: Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Retrieval & Search

1. Jimmy J. Lin
2. Jiawei Han
3. Ping Li
4. Hang Li
5. Tie-Yan Liu
6. Jure Leskovec
7. Stephan Günnemann
8. Ji-rong Wen

Citations in the corpus

• Inductive Representation Learning on Large Graphs (2017, neutral): “We first describe the GraphSAGE embedding generation (i.e., forward propagation) algorithm, which generates embeddings for nodes assuming that the GraphSAGE model parameters are already learned (Section 3.1).”
• Generating Wikipedia by Summarizing Long Sequences (2018, methodology): “An algorithm similar to PageRank (Page et al., 1999) is then used to compute the ranking of text units.”
• LINE: Large-scale Information Network Embedding (2015, methodology): “As the importance of the vertices in the network may be different, we introduce λi in the objective function to represent the prestige of vertex i in the network, which can be measured by the degree or estimated through algorithms such as PageRank [15].”
• Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies (2018, methodology): “TextRank uses an unsupervised sentence-ranking approach similar to Google PageRank (Page et al., 1999).”
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank (2018, methodology): “This algorithm adds a chance of teleporting back to the root node, which ensures that the PageRank score encodes the local neighborhood for every root node (Page et al., 1998).”
• GenDICE: Generalized Offline Estimation of Stationary Values (2020, methodology): “Off-line PageRank (OPR) The celebrated PageRank algorithm (Page et al., 1999) defines the ranking of a web page in terms of its asymptotic visitation probability under a random walk on the (augmented) directed graph specified by the hyperlinks.”
• LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, background): “To solve this problem, Page et al. (1998) suggest reserving some low probability for jumping to any node in the graph.”
• Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012, methodology): “To ground the GraphLab abstraction in a concrete problem, we will use the PageRank algorithm [31] as a running example.”
• Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (2019, methodology): “We chose Text-Rank (Mihalcea and Tarau, 2004) – an extractive non-parametric summarization system inspired by PageRank (Page et al., 1999) – since it is well performing for extractive tasks and could be easily adapted for our needs.”
• Supervised random walks: predicting and recommending links in social networks (2010, methodology): “PageRank [25] and variants like Personalized PageRank [17, 15] and Random Walks with Restarts [31] are popular methods for ranking nodes on graphs.”
• Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, methodology): “To tackle this challenge, we learn from graph-based extractive summarization models TextRank (Mihalcea and Tarau, 2004) and LexRank (Erkan and Radev, 2004), which are based on the PageR-ank (Page et al., 1999) algorithm.”
• Simple and Deep Graph Convolutional Networks (2020, methodology): “Finally, we recall that APPNP (Klicpera et al., 2019a) employs a similar approach to the initial residual connection in the context of Personalized PageRank.”
• A large‐scale study of the evolution of Web pages (2003, unknown): “as measured by the PageRank metric [11]).”
• A survey of trust and reputation systems for online service provision (2007, methodology): “We will denote the public PageRank of a page u as PR(u).”
• The anatomy of a large-scale social search engine (2010, background): “The Anatomy of a Large-Scale Social Search Engine Damon Horowitz Sepandar D. KamvarAardvark Stanford University damon@aardvarkteam.com sdkamvar@stanford.edu ABSTRACT We present Aardvark, a social search engine.”
• Know your neighbors: web spam detection using the web topology (2007, background): “Distinguishing be­tween desirable and undesirable content in such a system Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies…”
• Applications of Topic Models (2017, methodology): “They find the central words by using the PageRank [Page et al., 1999] algorithm, which finds words that are highly probable in the topic and appear frequently with many other words in the topic.”