  Graph Neural Networks (GNNs) have shown promising results on a broad spectrum
of applications. Most empirical studies of GNNs directly take the observed
graph as input, assuming the observed structure perfectly depicts the accurate
and complete relations between nodes. However, graphs in the real world are
inevitably noisy or incomplete, which could even exacerbate the quality of
graph representations. In this work, we propose a novel Variational Information
Bottleneck guided Graph Structure Learning framework, namely VIB-GSL, in the
perspective of information theory. VIB-GSL advances the Information Bottleneck
(IB) principle for graph structure learning, providing a more elegant and
universal framework for mining underlying task-relevant relations. VIB-GSL
learns an informative and compressive graph structure to distill the actionable
information for specific downstream tasks. VIB-GSL deduces a variational
approximation for irregular graph data to form a tractable IB objective
function, which facilitates training stability. Extensive experimental results
demonstrate that the superior effectiveness and robustness of VIB-GSL.
