The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Stochastic models for the Web graph
Abstract
The Web may be viewed as a directed graph each of whose vertices is a static HTML Web page, and each of whose edges corresponds to a hyperlink from one Web page to another. We propose and analyze random graph models inspired by a series of empirical observations on the Web. Our graph models differ from the traditional G/sub n,p/ models in two ways: 1. Independently chosen edges do not result in the statistics (degree distributions, clique multitudes) observed on the Web. Thus, edges in our model are statistically dependent on each other. 2. Our model introduces new vertices in the graph as time evolves. This captures the fact that the Web is changing with time. Our results are two fold: we show that graphs generated using our model exhibit the statistics observed on the Web graph, and additionally, that natural graph models proposed earlier do not exhibit them. This remains true even when these earlier models are generalized to account for the arrival of vertices over time. In particular, the sparse random graphs in our models exhibit properties that do not arise in far denser random graphs generated by Erdos-Renyi models.
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Cites
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“We show that whereas evolving copying models give rise to large numbers of bipartite cliques (as observed in the web graph), the number of such cliques in the evolving uniform and ACL models is likely to be small.”
BackgroundON A CLASS OF SKEW DISTRIBUTION FUNCTIONS
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The frequency distribution of scientific productivity
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The Web as a Graph: Measurements, Models, and Methods
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Self-citationTrawling the Web for emerging cyber-communities
Kumar, R., Raghavan, P., Rajagopalan, S., & Tomkins, A. (1999) · Computer Networks · doi:10.1016/s1389-1286(99)00040-7 · Cited by 1,000
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Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Dorogovtsev, S. N., & Mendes, J. F. F. (2002) · Advances in Physics · doi:10.1080/00018730110112519 · Cited by 3,171
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality
Newman, M. E. J. (2001) · Physical Review E · doi:10.1103/physreve.64.016132 · Cited by 2,613
Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005) · Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining · doi:10.1145/1081870.1081893 · Cited by 2,281
Metadata
- Authors
- Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., & Upfal, E.
- Venue
- Proceedings 41st Annual Symposium on Foundations of Computer Science
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Pages
- 57-65
- ISSN
- 0272-5428 (ISSN-L)
- OpenAlex
- W2115579680
- Cited by
- 676
- References
- 17
- FWCI
- 92.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., & Upfal, E. (2002). Stochastic models for the Web graph. Proceedings 41st Annual Symposium on Foundations of Computer Science. https://doi.org/10.1109/sfcs.2000.892065
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
