NetRL: Task-aware Network Denoising via Deep Reinforcement Learning

Abstract

Network data in real-world is error-prone, which results in inaccurate results when performing network analysis or modeling such as node classification and link prediction on these flawed networks. In this paper, we target at reconstructing a reliable network from a flawed network, named as network enhancement. Specifically, network enhancement aims to both detect the noisy links which are observed in the network but should not exist in the real world, and predict the missing links that indeed exist in the real world yet being unobserved in the network. Different from existing works that calculate a unified score to measure the above two kinds of links, we propose E-Net, an end-to-end graph neural network model, to leverage the mutual influence of the two tasks to achieve both the goals more effectively. Because on one hand, detecting noisy links can benefit the performance of predicting missing links; and on the other hand, predicting missing links can provide indirect supervision for detecting noisy links when the labels of the noisy links are unavailable. The experimental results on several datasets show that the proposed model obtains significant improvement for predicting missing links and detecting noisy links.