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Data-Driven Link Screening for Increasing Network Predictability
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DOI：10.1109/TKDE.2019.2955650.png)
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Abstract
En 中文
Prediction methods applied to digital network data offer powerful capabilities that have radically affected a host of industries and services. Specifically, numerous works have shown that the use of network information to predict focal node properties produces significantly more accurate results compared with exclusive reliance on other types of data. In this study, we propose that it may be possible to improve network-information-based predictions by identifying network links that actually carry predictive power for a given prediction task. For this purpose, we suggest a problem referred to as the problem of Increasing Network Predictability (INP) by data-driven link screening. To address this problem we develop a new algorithm with three different implementations. We find that the algorithm is robust and consistently outperforms baseline link selection methods. We thus suggest that our algorithm has the potential to improve the efficacy of network data use for classification purposes.
Keywords:
Training
Task analysis
Prediction algorithms
Tagging
Social networking (online)
Supervised learning
Industries
Data science
networks
network science
network learning
network analysis
network data
social networks
classification
link screening
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