Skip to Main content Skip to Navigation
New interface
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [66 references]  Display  Hide  Download
Contributor : Quang Vinh Dang Connect in order to contact the contributor
Submitted on : Wednesday, March 11, 2020 - 3:54:14 PM
Last modification on : Wednesday, June 8, 2022 - 9:54:02 AM
Long-term archiving on: : Friday, June 12, 2020 - 4:10:45 PM


Files produced by the author(s)


  • HAL Id : hal-02505585, version 1


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



Record views


Files downloads