Link Prediction Based on Precision Optimization

Gu, Shensheng; Chen, Ling

doi:10.1007/978-981-10-3966-9_14

Cited by 3 publications

(1 citation statement)

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“…In [12], the authors solved the link prediction problem as a binary classification task, where they set the precision as an objective function, and then transform the link prediction problem into an optimization problem where the authors defined a feature set for each edge. This set contains state-of-the-art metrics, the used class label was +1 for existing links and −1 for non-existing links.…”

Section: Introductionmentioning

confidence: 99%

Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

2022

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The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

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Section: Introductionmentioning

confidence: 99%