What are weighted similarity metrics in link prediction?
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Weighted similarity metrics in link prediction involve quantitatively measuring the dissimilarity of weighted networks to predict missing links or future relationships accurately. Various approaches have been proposed to extend unweighted similarity indices to weighted networks, such as the WD-metric, which captures the influence of weight on network structure . Additionally, the reliable-route method extends local similarity indices to predict both link existence and weights, showing superior performance in weight prediction compared to other methods . Furthermore, the Weighted Triple Loss function and Rule Loss function are introduced for knowledge graph embeddings, considering the weights of facts in the knowledge graph to improve link prediction accuracy . These weighted similarity metrics play a crucial role in enhancing the performance of link prediction algorithms in weighted networks.
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| Papers (5) | Insight |
|---|---|
97 Citations | Weighted similarity metrics in link prediction include the reliable-route weighted resource-allocation index, which extends local similarity indices to predict both link existence and weights with high accuracy. |
11 Citations | Weighted similarity metrics in link prediction consider numerical weights on facts to measure the degree of association or truth of a triple, enhancing prediction accuracy in knowledge graphs. |
| Weighted similarity metrics in link prediction include algorithms that predict weights in multiplex networks by considering associations from multiple layers, enhancing accuracy in weight prediction compared to traditional indices. | |
3 Citations | Weighted similarity metrics in link prediction include the WD-metric, which quantitatively measures dissimilarity in weighted networks by considering the influence of weights on network structure. |
1 Citations | The paper introduces a novel similarity measure based on eigenvectors and shortest distance for link prediction, outperforming baseline algorithms in AUC, precision, prediction-power, and precision@K metrics. |
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