The Anatomy of a Large-Scale Hypertextual Web Search Engine

By Sergey Brin, Lawrence Page. 1998

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “s algorithms. As a result, we are exposed daily to rankings that are based at least partially on popularity, from bestseller lists to the results returned by search engines in response to our queries [5]. The usefulness of such rankings is predicated on the wisdom of the crowd [33]: high-quality cho”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, neutral): “2] (Brin & Page, 1998).”
Community detection in graphs (2009, methodology): “In the latter case, the random walk process is modified by introducing a teleportation probability τ , to guarantee ergodicity, just like in Google’s PageRank algorithm (Brin and Page, 1998).”
Estimating the Spectral Density of Large Implicit Matrices (2018, methodology): “The inﬂuential PageRank algorithm (Brin and Page, 1998) examines the principal eigenvector of a graph for the most “important” vertices.”
Sybil-Resilient Online Content Voting (2009, methodology): “However, we note that many ranking algorithms utilize both the number of votes and the average value of votes to determine an object’s rank [2, 12].”
Optimizing web search using social annotations (2007, background): “For example, Google’s search engine [28] took the anchor text as its metadata to improve the performance of search.”
LETOR: A benchmark collection for research on learning to rank for information retrieval (2010, background): “(3) The node “docinfo” (line 25-40 in Figure 3) plays a role similar to the forward index [3], but is limited to the query terms and the selected documents.”
A Survey of Link Prediction in Social Networks (2011, background): “Chung and Zhao [13] showed that the Pagerank [11] measures that is used for web-page ranking has inherent relationship with the hitting time.”
Learning block importance models for web pages (2004, methodology): “Another one is to improve link analysis algorithms, such as PageRank [2].”
Patent citation data in social science research: Overview and best practices (2017, unknown): “This method is directly inspired from Google’s “random surfer” model and takes into account the fact that different citations weigh differently depending on the importance of the citing documents (Brin & Page, 1998).”
Piccolo: Building Fast, Distributed Programs with Partitioned Tables (2010, methodology): “The PageRank algorithm [11] tuple PageID(site , page)”
A scalable, commodity data center network architecture (2008, background): “A query to a web search engine often requires parallel communication wi th every node in the cluster hosting the inverted index to return t he most relevant results [7].”
The science of science: from the perspective of complex systems (2017, methodology): “A method based on the PageRank [173] and the Hyperlink-Induced Topics Search (HITS) [248] algorithms is then proposed to rank journals.”