Active Learning for Graphs with Noisy Structures
Hongliang Chi Cong Qi Suhang Wang Yao Ma
Graph Neural Networks (GNNs) have seen significant success in tasks such as
node classification, largely contingent upon the availability of sufficient
labeled nodes. Yet, the excessive cost of labeling large-scale graphs led to a
focus on active learning on graphs, which aims for effective data selection to
maximize downstream model performance. Notably, most existing methods assume
reliable graph topology, while real-world scenarios often present noisy graphs.
Given this, designing a successful active learning framework for noisy graphs
is highly needed but challenging, as selecting data for labeling and obtaining
a clean graph are two tasks naturally interdependent: selecting high-quality
data requires clean graph structure while cleaning noisy graph structure
requires sufficient labeled data. Considering the complexity mentioned above,
we propose an active learning framework, GALClean, which has been specifically
designed to adopt an iterative approach for conducting both data selection and
graph purification simultaneously with best information learned from the prior
iteration. Importantly, we summarize GALClean as an instance of the
Expectation-Maximization algorithm, which provides a theoretical understanding
of its design and mechanisms. This theory naturally leads to an enhanced
version, GALClean+. Extensive experiments have demonstrated the effectiveness
and robustness of our proposed method across various types and levels of noisy
graphs.