In short, a library for computing dynamic pagerank. See [1] and [2] for further details.
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Ryan Rossi and David Gleich: Dynamic PageRank using Evolving Teleportation, Algorithms and Models for the Web Graph, vol. 7323 of LNCS, pages 126-137. Springer, 2012.
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David F. Gleich, Ryan A. Rossi, A Dynamical System for PageRank with Time-Dependent Teleportation, Internet Mathematics, 10:1-2, 188-217, 2014.
These codes are research prototypes and may not work for you. No promises. But do email if you run into problems.
Unzip data into dynamic_pagerank directory
Start matlab in the directory where you unzipped the dynamic_pagerank.zip file
$ matlab
>> setup_paths
>> load('data/wiki-24hours');
This should work on Mac OSX (Lion tested) and Ubuntu linux (10.10 tested) with Matlab R2011a.
>> v = normcols(v);
>> X = dynamic_pagerank(A,v);
See examples.m for additional examples
Please let us know if you run into any issues.
The package is organized by directory
/
: All of the main matlab codes (dynamic_pagerank.m,...)
ranking
: dynamic ranking codes and figures
forecasting
: simple models for prediction using Dynamic PageRank
clustering
: experimental codes for identifying trends and similar vertices
causality
: codes for computing Granger causality between vertices
data
: graphs, precomputed data, and script files for extracting and parsing page views
web
: this information and all the figures
| Experiment | Description | Figure |
|---|---|---|
fluctuating_interest.m |
PageRank dynamical system analytical solution | Fig. 2 |
plot_vertex_yxlims.m |
PageRank dynamical system analytical solution | Fig. 3 |
ranking/compute_isim.m |
The intersection similarity plot | Fig. 5 |
dpr_timeseries.m |
Dynamic PageRank time-series plot | Fig. 6-7 |
forecasting/print_preds_table.m |
Performance of Dynamic PageRank for prediction | Tab. 3 |
clustering/dpr_clustering.m |
Cluster dynamic score trends, vertices w/ similar behavior | Fig. 8 |
causality/prt_causality.m |
Granger causality between vertices | Tab. 4 |