We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as negative link prediction or NLP consists in estimating the disappearance of a link in the network. We propose a data-driven approach to estimate the appearance/disappearance of edges. Our solution is based on a regularized optimization problem for which we prove existence and uniqueness of the optimal solution.

Link Prediction: A Graphical Model Approach

Alpago Daniele;Zorzi Mattia;Ferrante Augusto
2020

Abstract

We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as negative link prediction or NLP consists in estimating the disappearance of a link in the network. We propose a data-driven approach to estimate the appearance/disappearance of edges. Our solution is based on a regularized optimization problem for which we prove existence and uniqueness of the optimal solution.
2020
European Control Conference 2020, ECC 2020
18th European Control Conference, ECC 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3353379
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