Temporal Link Prediction in Knowledge Networks

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Temporal Link Prediction in Knowledge Networks by Julia Perl.

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Temporal Link Prediction in Knowledge Networks

  1. 1. Temporal Link Prediction in Knowledge Networks Julia Perl, Jérôme Kunegis Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 1 of 18
  2. 2. Wikipedia Knowledge Network ➔ Knowledge Network consists of articles which are interlinked ● ● Nodes = Wikipedia articles Links = Links between Wikipedia articles Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 2 of 18
  3. 3. Wikipedia Knowledge Network “Appropriate links provide instant pathways to locations within and outside the project that are likely to increase readers' understanding of the topic at hand. When writing or editing an article, it is important to consider not only what to put in the article, but what links to include to help the reader find related information” [...] “An article is said to be underlinked if words are not linked that are needed to aid understanding of the article.” [...] “An overlinked article contains an excessive number of links, making it difficult to identify links likely to aid the reader's understanding significantly.” [http://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Linking accessed last on Dec. 14, 2013] Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 3 of 18
  4. 4. Research Questions Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 4 of 18
  5. 5. Our Research Questions How to predict new interlinks between articles to avoid underlinking? (Link Prediction) How to predict interlinks between articles that should be removed to avoid overlinking or wrong links? (Unlink Prediction) Hypothesis Structural changes can be predicted from the network structure. Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 5 of 18
  6. 6. Link and Unlink Prediction Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 6 of 18
  7. 7. Link and Unlink Prediction The Snapshot View Training Additions Removals Link Prediction Problem Unlink Prediction Problem Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 7 of 18
  8. 8. Link and Unlink Prediction The Snapshot View ● ➢ Unlink prediction is more difficult than link prediction The snapshot view does not provide information on links that have been removed. Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 8 of 18
  9. 9. Temporal Link and Unlink Prediction Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 9 of 18
  10. 10. Prediction Models ● Snapshot View Model 0: Baseline Model Snapshot Model: measures computed from adjacency matrix ● Temporal data Model 1: Add-Remove Model Classic adjacency matrix and removal adjacency matrix ● ● Model 2: Temporal Add-Remove Model Hypothesis: Usage of temporal information improves the Temporal Values in adjacency and removal adjacency matrix classification of links and unlinks significantly. Model 3: Temporal Preferential Attachment & Preferential Detachment Estimate growth and decay for each node based on temporal evolution Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 10 of 18
  11. 11. Model 0: Baseline Model Snapshot View: all measures are computed from the adjacency matrix Training Adjacency matrix A, ● Compute characteristics from A ● ● ● d(i) Degree of article i CN(i,j) Number of common neighbors of articles i and j P3(i,j): Number of paths of length 3 between articles i and j Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 11 of 18
  12. 12. Model 1: Add-Remove Model Classic adjacency matrix and removal adjacency matrix Adjacency matrix A + Removal adjacency matrix A⁻ ● Compute characteristics from A ● ● ● ● d⁻(i) Remove-degree of article i dRatio(i) Ratio of deletes and adds CN⁻(i,j) P3⁻(i,j) Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 12 of 18
  13. 13. Model 2: Temporal Add-Remove Model Temporal Values in adjacency and removal adjacency matrix Difference between two articles that have recently connected with the same other articles or long ago. Seconds ➔ Years More recent common neighbors → higher likelihood for link Functions , decreasing in time Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 13 of 18
  14. 14. Model 3: Temporal PA & PD Estimate growth and decay for each node based on temporal evolution ● Preferential Attachment (PA): number of new links proportional to node degree ➔ ● Disregards temporal evolution Based on temporal evolution estimate number of ● ● new links (Link Prediction) removed links (Unlink Prediction) Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 14 of 18
  15. 15. Set Up Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 15 of 18
  16. 16. Set Up ● Five large Wikipedia datasets ● Our datasets comprise several year (up to ten) of data. ● Compute AUC-value of features for Link and Unlink Prediction Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 16 of 18
  17. 17. Ready for your feedback and other interesting datasets :) Julia Perl jpreusse@uni-koblenz.de CSS Workshop 12/16/13 17 of 18

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