Exploiting Non-visible Relationship in Link Prediction Based on Asymmetric Local Random Walk

Fan, Cheng; Li, Dong; Teng, Yiping; Fan, Dongwan; Ding, Guohui

doi:10.1007/978-3-319-70139-4_74

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Structural-based methods do not rely on the individual information of the nodes but based on the topological information of the graph [41,21]. An early attempt is credited to [1] that defines the Adamic-Adar measure between node i and node j as: (1) where Γi is the set of direct neighbors of the node i.…”

Section: Link-prediction Problemmentioning

confidence: 99%

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

Dang

2020

Integrated Science in Digital Age 2020

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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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