Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks

Gao, Mengzhou; Jiao, Pengfei; Lu, Ruili; Wu, Huaming; Wang, Yinghui; Zhao, Zhidong

doi:10.1109/tcss.2022.3176928

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…However, the method is mainly developed for single-layer networks. Gao et al [24] developed a network embedding method called interactive learning across relations that exploits existing multiple types of relational data to predict inductive links (new or unobserved relation) between existing nodes instead of inter-or intra-layer links in a known layer.…”

Section: Literature Reviewmentioning

confidence: 99%

Reconstructing Sparse Multiplex Networks with Application to Covert Networks

Bichler

et al. 2023

Entropy

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Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation–Maximization–Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation–Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.

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Section: Literature Reviewmentioning

confidence: 99%