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

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Integrated Science in Digital Age 2020 (ICIS 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 136))

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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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Notes

  1. 1.

    https://xgboost.readthedocs.io.

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Authors and Affiliations

  1. Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Quang-Vinh Dang

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  1. Quang-Vinh Dang
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Correspondence to Quang-Vinh Dang .

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Editors and Affiliations

  1. Institute of Certified Specialists (ICS), Perm, Russia

    Tatiana Antipova

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Dang, QV. (2021). Link-Sign Prediction in Signed Directed Networks from No Link Perspective. In: Antipova, T. (eds) Integrated Science in Digital Age 2020. ICIS 2020. Lecture Notes in Networks and Systems, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-49264-9_26

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