Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory
arxiv(2024)
摘要
Network control theory (NCT) offers a robust analytical framework for
understanding the influence of network topology on dynamic behaviors, enabling
researchers to decipher how certain patterns of external control measures can
steer system dynamics towards desired states. Distinguished from other
structure-function methodologies, NCT's predictive capabilities can be coupled
with deploying Graph Neural Networks (GNNs), which have demonstrated
exceptional utility in various network-based learning tasks. However, the
performance of GNNs heavily relies on the expressiveness of node features, and
the lack of node features can greatly degrade their performance. Furthermore,
many real-world systems may lack node-level information, posing a challenge for
GNNs.To tackle this challenge, we introduce a novel approach, NCT-based
Enhanced Feature Augmentation (NCT-EFA), that assimilates average
controllability, along with other centrality indices, into the feature
augmentation pipeline to enhance GNNs performance. Our evaluation of NCT-EFA,
on six benchmark GNN models across two experimental setting. solely employing
average controllability and in combination with additional centrality metrics.
showcases an improved performance reaching as high as 11
demonstrate that incorporating NCT into feature enrichment can substantively
extend the applicability and heighten the performance of GNNs in scenarios
where node-level information is unavailable.
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