Document Type

Article

Publication Date

6-11-2018

Publication Title

IEEE Access

First page number:

1

Last page number:

9

Abstract

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks.

Keywords

Deep learning; Link prediction; Dynamic networks; Weak estimators; Similarity metrics

Disciplines

Artificial Intelligence and Robotics

File Format

application/pdf

File Size

1.067 Kb

Language

English

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