Positive-Unlabeled Learning for Network Link Prediction

نویسندگان

چکیده

Link prediction is an important problem in network data mining, which dedicated to predicting the potential relationship between nodes network. Normally, link based on supervised classification will be trained a dataset consisting of set positive samples and negative samples. However, well-labeled training datasets with annotations are always inadequate real-world scenarios, contain large number unlabeled that may hinder performance model. To address this problem, we propose positive-unlabeled learning framework representation for only using We first learn vectors method. Next, concatenate node pairs then feed them into different classifiers predict whether exists or not. alleviate imbalance enhance precision, adopt three types (PU) strategies improve traditional classifier estimation, bagging strategy reliable sampling. conduct experiments compare PU methods discuss their influence results. The experimental results demonstrate has impact predictive performances promotion effects vary structures.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2022

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math10183345