kleinberg_web — Kleinberg web query graphs (1999)
- Description
Web graphs constructed by J. Kleinberg by expanding the response set of 200 pages returned by a search engine for a query, as in his hub/authority (HITS) algorithm: the root set of pages is expanded with the pages that link to them and the pages they link to. Nodes are web pages, and directed edges are hyperlinks. Many of the pages have no links to or from the other pages in the distributed data, and appear as isolated nodes. The graphs were distributed on Kleinberg's course page (Cornell CS685, 2002), and adapted for Pajek by V. Batagelj (2006). They were used, e.g., by A. N. Langville and C. D. Meyer to study the reordering of the PageRank problem. The data were distributed in Pajek format on the Pajek datasets page of V. Batagelj and A. Mrvar, from which this version was obtained.
california: pages matching the query "California".epa: pages linking to www.epa.gov.
The networks have the following properties:
- Nodes:
name, the URL of the page.
- Tags
- Informational Web graph Unweighted Metadata
- Citation
-
- J. M. Kleinberg, "Authoritative sources in a hyperlinked environment." Journal of the ACM 46(5), 604-632 (1999) 10.1145/324133.324140 Sci-Hub
- A. N. Langville, C. D. Meyer, "A Reordering for the PageRank Problem." SIAM Journal on Scientific Computing 27(6), 2112-2120 (2006) 10.1137/040607551 Sci-Hub
- Upstream URL OK
- https://web.archive.org/web/20090301185253/http://vlado.fmf.uni-lj.si/pub/networks/data/mix/mixed.htm
- Networks
-
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv california 9,664 16,150 1.67 8.49 19.98 100.13 -0.23 0.02 13 0.61 Directed Unipartite name 152 KiB 198 KiB 196 KiB 202 KiB epa 4,772 8,965 1.88 9.61 13.80 31.99 -0.30 0.01 10 0.89 Directed Unipartite name 75 KiB 92 KiB 91 KiB 94 KiB