A Structural-Clustering Based Active Learning for Graph Neural Networks
Ricky Maulana Fajri Yulong Pei Lu Yin Mykola Pechenizkiy
In active learning for graph-structured data, Graph Neural Networks (GNNs)
have shown effectiveness. However, a common challenge in these applications is
the underutilization of crucial structural information. To address this
problem, we propose the Structural-Clustering PageRank method for improved
Active learning (SPA) specifically designed for graph-structured data. SPA
integrates community detection using the SCAN algorithm with the PageRank
scoring method for efficient and informative sample selection. SPA prioritizes
nodes that are not only informative but also central in structure. Through
extensive experiments, SPA demonstrates higher accuracy and macro-F1 score over
existing methods across different annotation budgets and achieves significant
reductions in query time. In addition, the proposed method only adds two
hyperparameters, $\epsilon$ and $\mu$ in the algorithm to finely tune the
balance between structural learning and node selection. This simplicity is a
key advantage in active learning scenarios, where extensive hyperparameter
tuning is often impractical.