Robust Network Enhancement from Flawed Networks

Jiarong Xu, Yang Yang, Chunping Wang, Zongtao Liu, Jing Zhang, Lei CHEN, Jiangang Lu

Abstract:

Network data in real-world tends to be error-prone due to
incomplete sampling, imperfect measurements, etc.; this in turn 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 aim to reconstruct a reliable network from a
flawed, undirected, unweighted network, a process referred to network
enhancement. More specifically, network enhancement aims to detect
the noisy links that are observed in the network but should not exist in
the real world, as well as to predict the missing links that do indeed
exist in the real world yet remain unobserved. While some attempts
have been made to detect either noisy links or missing links, few of
these works have considered unifying these two tasks, even though
they are inter-dependent and capable of mutually boosting each others’
performance. In this paper, we therefore propose E-Net, an end-toend graph neural network model, to leverage the mutual influence of
these two tasks in order to achieve both goals more effectively. On one
hand, detecting noisy links can benefit the performance of missing link
prediction, while on the other hand, predicting missing links can provide
indirect supervision for detecting noisy link detection when the labels of
these noisy links are unavailable. Moreover, by proposing a subgraph
extraction mechanism based on random walk with restart, the model
can be scaled up to large networks and is able to preserve the local
and global structural characteristics. The experimental results on several
types of large networks demonstrate that the proposed model obtains an
improvement of 10.7% on average in terms of F1 for predicting missing
links, along with an average of 3.7% improvement in terms of precision
for detecting noisy links compared with the state-of-the-art baselines.