Winners don't take all: Characterizing the competition for links on the web

By David M. Pennock, Gary William Flake, Steve Lawrence, Eric J. Glover, C. Lee Giles. Proceedings of the National Academy of Sciences of the United States of America, 2002

497 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e9d1335fddddab3180ffaa8946b9fa2325df4294

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In Statistical and Nonlinear Physics

Citations in the corpus

Knowledge networks across Europe: which distance matters? (2009, background): “In particular, Paci and Usai (2000) and Breschi and Lissoni (2004) have developed systematic analyses of patenting activity throughout Europe at different NUTS levels, showing the existence of significant clustering phenomena (whose agglomeration indexes are even higher than those registered by hi”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “[223] changed the BA model of preferential attachment including a uniform attachment pr obability.”
A Survey of Statistical Network Models (2009, unknown): “Alternative graph generation mechanisms appear every day—R–MAT [60],‘winners don’t take all’ [242],‘forest fire’ [194],‘butterfly’ [212] and RTG [10], to name a few.”
The Structure and Function of Complex Networks (2003, background): “The World Wide Web, for instance, shows a power-law degree distribution overall but unimodal distributions within domains [338].”
R-MAT: A Recursive Model for Graph Mining (2004, unknown): “[20] observed deviations from power-laws for the Web graph, which are well-modeled by the truncated, discretized lognormal (“DGX”) distribution of Bi et al.”
Bibliometrics to webometrics (2008, background): “In the latter case a second factor must have been at work, such as pre-knowledge of the existence of the page [93].”
Kronecker Graphs: An Approach to Modeling Networks (2008, methodology): “…attachment model, all somehow employing the “rich get richer” type mechanism, e.g., the “copying model”(Kumar et al., 2000), the “winner does not take all” model (Pennock et al., 2002), the “forest fire” model (Leskovec et al., 2005b), the “random surfer model” (Blum et al., 2006), etc.”
The Matthew effect in empirical data (2014, background): “Soon to follow were reports of preferential attachment and resulting scaling behaviour in the protein network evolution [61] and the evolution of metabolic networks [62, 63], the Internet [57] and World Wide Web [20], the accumulation of citations [57, 64–70] and scientific impact [71, 72], the maki”
Topical interests and the mitigation of search engine bias (2005, background): “We will also refer to this quantity as the traﬃc to the same page.”
Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, unknown): “Later work by Pennock and others [168] showed that winners do not take all; a slight modification to the model of Barabasi and others show much better fit to Web data.”
Topic-sensitive PageRank (2002, methodology): “The recent work of Chakrabarti et al. [10] and Pennock et al. [ 27 ] demonstrates that the properties of the web graph are sensitive to page topic.”
Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication (2005, background): “Deviations from the power-law pattern have been noticed [23], which can be explained by the “DGX” distribution [5].”