Unsupervised Link Prediction Algorithm: Difference between revisions

 
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A [[Unsupervised Link Prediction Algorithm]] is a [[Data-Driven Link Prediction Algorithm]] that is an [[Unsupervised Data-Driven Multiclass Classification Algorithm]].
A [[Unsupervised Link Prediction Algorithm]] is a [[Data-Driven Link Prediction Algorithm]] that is an [[Unsupervised Multiclass Classification Algorithm]].
* <B>Context:</B>
* <B>Context:</B>
** It can be implemented into a [[Unsupervised Data-Driven Link Prediction System]] (that can solve a [[Unsupervised Data-Driven Link Prediction Task]]).
** It can be applied by an [[Unsupervised Link Prediction System]] (that can solve an [[Unsupervised Link Prediction Task]]).
* <B>See:</B> [[Supervised Data-Driven Link Prediction Algorithm]].
** …
* <B>Counter-Example(s):</B>
** a [[Supervised Link Prediction Algorithm]].
** an [[Unsupervised Sequence Tagging Algorithm]].
* <B>See:</B> [[Heuristic Link Prediction Algorithm]].
 
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==References==


===2010===
== References ==
* ([[Lichtenwalter & al, 2010]]) => Ryan N. Lichtenwalter, Jake T. Lussier, and [[Nitesh V. Chawla]]. (2010). "New Perspectives and Methods in Link Prediction." In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining ([[KDD-2010]]).
 
=== 2011 ===
* ([[2011_LinkPredictionviaMatrixFactoriz|Menon & Elkan, 2011]]) ⇒ [[Aditya Krishna Menon]], and [[Charles Elkan]]. ([[2011]]). “[http://cseweb.ucsd.edu/~elkan/ECML2011LinkPrediction.pdf Link Prediction via Matrix Factorization].” In: [[Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II]]. ISBN:978-3-642-23782-9
** QUOTE: At a high level, existing [[data-driven link prediction algorithm|link prediction models]] fall into two classes: [[Unsupervised Link Prediction Algorithm|unsupervised]] and [[supervised link prediction algorithm|supervised]]. [[Unsupervised Link Prediction Algorithm|Unsupervised models]] compute [[score]]s for [[pairs of nodes]] based on [[topological properties of the graph]]. For example, one such score is the [[number of common neighbours]] that [[node pair|two nodes]] [[share]]. Other popular scores are the [[Adamic-Adar]] [1] and [[Katz score]] [22]. These models use [[predefined score]]s that are [[invariant]] to the specific [[structure]] of the [[input]] [[graph]], and thus do not involve any [[learning]]. [[supervised link prediction algorithm|Supervised models]], on the other hand, ... <BR>We list some popular [[data-driven link prediction algorithm|approach]]es: ...
<BR>
* ([[Wang, Predeschi et al., 2011]]) ⇒ Dashun Wang, [[Dino Pedreschi]], Chaoming Song, Fosca Giannotti, and [[Albert-Laszlo Barabasi]]. ([[2011]]). “Human Mobility, Social Ties, and Link Prediction.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ([[KDD-2011]]).
** QUOTE: ... We report below the results obtained in our [[link prediction analysis]] in both cases. Another dimension of our study is the kind of classification used. Adhering to the [[machine learning terminology]] [23], [[we]] consider both [[Unsupervised Link Prediction Algorithm|unsupervised]] and [[Supervised Link Prediction Algorithm|supervised link prediction]]: ...
 
=== 2010 ===
* ([[Lichtenwalter et al., 2010]]) Ryan N. Lichtenwalter, Jake T. Lussier, and [[Nitesh V. Chawla]]. ([[2010]]). “New Perspectives and Methods in Link Prediction.In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ([[KDD-2010]]).
** QUOTE: ... method appropriate for the observed imbalance. Our careful consideration of the above issues ultimately leads to a completely general framework that outperforms [[Unsupervised Link Prediction Algorithm|unsupervised link prediction method]]s by more than 30% AUC. ...


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[[Category:Concept]]

Latest revision as of 14:10, 2 August 2022

A Unsupervised Link Prediction Algorithm is a Data-Driven Link Prediction Algorithm that is an Unsupervised Multiclass Classification Algorithm.



References

2011


2010

  • (Lichtenwalter et al., 2010) ⇒ Ryan N. Lichtenwalter, Jake T. Lussier, and Nitesh V. Chawla. (2010). “New Perspectives and Methods in Link Prediction.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010).
    • QUOTE: ... method appropriate for the observed imbalance. Our careful consideration of the above issues ultimately leads to a completely general framework that outperforms unsupervised link prediction methods by more than 30% AUC. ...