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Link-Sign Prediction in Signed Directed Networks from No Link Perspective

Abstract : Predicting future sign of connections in a network is an important task for online systems such as social networks, e-commerce and other services. Several research studies have been presented since the early of this century to predict either the existence of a link in the future or the property of the link. In this study we present a new approach that combine both families by using machine learning techniques. Instead of focusing on the established links, we follow a new research approach that focusing on no-link relationship. We aim to understand the move between two states of no-link and link. We evaluate our methods in popular real-world signed networks datasets. We believe that the new approach by understanding the no-link relation has a lot of potential improvement in the future.
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https://hal.inria.fr/hal-02505585
Contributor : Quang Vinh Dang <>
Submitted on : Wednesday, March 11, 2020 - 3:54:14 PM
Last modification on : Wednesday, March 11, 2020 - 4:06:14 PM
Long-term archiving on: : Friday, June 12, 2020 - 4:10:45 PM

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  • HAL Id : hal-02505585, version 1

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Quang-Vinh Dang. Link-Sign Prediction in Signed Directed Networks from No Link Perspective. Lecture Notes in Networks and Systems, Springer Verlag, 2020. ⟨hal-02505585⟩

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