Unsupervised Link Prediction Algorithm: Difference between revisions
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* ([[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]]). | * ([[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]]: ... | ** 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 === | === 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]]). | * ([[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. ... | ** 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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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.
- Context:
- It can be applied by an Unsupervised Link Prediction System (that can solve an Unsupervised Link Prediction Task).
- …
- Counter-Example(s):
- See: Heuristic Link Prediction Algorithm.
References
2011
- (Menon & Elkan, 2011) ⇒ Aditya Krishna Menon, and Charles Elkan. (2011). “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 link prediction models fall into two classes: unsupervised and supervised. Unsupervised models compute scores for pairs of nodes based on topological properties of the graph. For example, one such score is the number of common neighbours that two nodes share. Other popular scores are the Adamic-Adar [1] and Katz score [22]. These models use predefined scores that are invariant to the specific structure of the input graph, and thus do not involve any learning. Supervised models, on the other hand, ...
We list some popular approaches: ...
- QUOTE: At a high level, existing link prediction models fall into two classes: unsupervised and supervised. Unsupervised models compute scores for pairs of nodes based on topological properties of the graph. For example, one such score is the number of common neighbours that two nodes share. Other popular scores are the Adamic-Adar [1] and Katz score [22]. These models use predefined scores that are invariant to the specific structure of the input graph, and thus do not involve any learning. Supervised models, on the other hand, ...
- (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 and 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 methods by more than 30% AUC. ...