Tango with Drift: A Counteractive Field for Compact Graph Learning

Graph representation learning has become a cornerstone of modern machine learning. Yet, inhibiting task-irrelevant and misleading signals on graphs remains highly challenging. Existing methods mainly focus on the static outcome level, \it i.e., by regularizing either graph structures or final representations after detrimental signals have already diffused and become entangled with predictive cues. This not only makes post-hoc removal intrinsically difficult, but also risks collateral damage to task-relevant structures. In this paper, we propose CounterDiff, which casts compact graph learning as a propagation-refinement problem under a graph stochastic partial differential equation (SPDE) view. CounterDiff characterizes the spectral signature of detrimental propagation and enforces coherent counteractions while preserving the predictive flow. We extensively evaluate CounterDiff on diverse graph learning tasks, and the consistent gains highlight the importance of propagation-level intervention for compact yet predictive graph learning.