R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for cyber communities. In Proc. 8th WWW Conference, 1999. Home/Search Document Not in Database Summary Related Articles Check |
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Who Links to Whom: Mining Linkage between Web Sites - Bharat, Chang, Henzinger, Ruhl (2003) (16 citations) (Correct)
....instead there is a core of hosts whose average distance is smaller than 4 that contains most of the pages. 4. Inverse Power Law Distributions Previous papers have observed that various properties of the web graph follow a Zipfian distribution (a function of the form 1=n k ) Kumar et al. [14] show that the fraction of web pages with indegree i is roughly proportional to 1=i . Barbarasi and Albert [4] report a Zipfian exponent of 2.1 for the indegree distribution and they also show that the fraction of web pages with outdegree i is roughly proportional 2:45 . In a recent paper ....
....Brewington [7] developed a different model of web page changes. In the area of web graph analysis, Barbarasi et al. [4] estimated the diameter of the Web and presented the Zipfian 7 indegree and outdegree distributions of web pages (see also comments by Adamic et al. [2] on this work. Kumar et al. [14] presented various properties of the web graph, one of them being the Zipfian distribution of indegrees as well. They also showed that the web contains a large number of small bipartite cliques. Kleinberg et al. [12] presented a copy model, which was analyzed in detail in [13] Broder et al. 8] ....
R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for cyber communities. In Proc. 8th WWW Conference, 1999.
Simulated Web Graphs - Che (Correct)
....degree distributions, connected components, and Web macroscopic structure have been studied [4] Figure 1.3: In degrees follow a power law with exponent 2.1. The law also holds if only off site (or remote only ) edges are considered 1.2. 1 Degree distributions in the Web graph Kurmar et al. [12] used a pruned data set from 1997 containing about 40 million pages to study structural properties of the Web graph. Their study suggested that the distribution of in degrees and out degrees follow power laws, i.e. the probability that any node has in degree i is proportional to c i x for some ....
....200 million pages and 1.5 billion hyperlinks. They generated the in degree and out degree distributions. The exponent for the power law for in degrees is 5 consistently around 2.1 (see Figure 1. 3, which is from [4] confirming previous reports on power laws of in degree distributions [12]. Distributions of out degrees also exhibit a power law, with the exponent of 2.7 (see Figure 1.4, from [4] Broder et al. 4] also found that the initial portion of the outdegree distribution deviates significantly from the power law, suggesting that pages with low out degree may follow a ....
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R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. "Trawling the Web for cyber communities ", Computer Networks, 31: 1481-1493, 1999.
Self-Similarity in the Web - Dill, Kumar, McCurley, Rajagopalan.. (2001) (24 citations) Self-citation (Kumar Rajagopalan Tomkins) (Correct)
....of the hyperlinked structure of the web. Broadly, there are two (very related) lines of research that have emerged. The first one is more theoretical and is concerned with proposing stochastic models that explain the hyperlink structure of the web [27, 7, 1] The second line of research [13, 7, 3, 28] is more empirical; new experiments are conducted that either validate or refine existing models. There are several driving applications that motivate (and are motivated by) a better understanding of the neighborhood structure on the web. In particular, the second generation of data service ....
....are motivated by) a better understanding of the neighborhood structure on the web. In particular, the second generation of data service applications on the web including advanced search applications [16, 17, 10] browsing and information foraging [14, 39, 15, 40, 19] community extraction [28], taxonomy construction [30, 29] have all taken tremendous advantage of knowledge about the hyperlink structure of the web. As just one example, let us mention the community extraction algorithm of [28] In this algorithm, a characterization of degree sequences within web page neighborhoods ....
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R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for cyber communities. Proc 8th WWW, 1999.
A Significant Improvement to Clever Algorithm in Hyperlinked.. - Wang (2002) (Correct)
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R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the Web for Cyber Communities. Proc. 8th International World Wide Web Conference,1999.
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