Beyond Graph Priors: A Co-Evolving Framework Under Uncertainty for Enterprise Resilience Assessment

Abstract

Assessing enterprise resilience under uncertainty necessitates capturing both intrinsic attributes and evolving inter-enterprise dependencies. However, real-world enterprise systems pose substantial structural challenges: redundant or loosely correlated links can trigger spurious relational inferences, while missing or latent dependencies often hinder the propagation of informative signals. Moreover, most existing approaches adopt static graph priors or decouple structural refinement from semantic learning, lacking a co-evolutionary paradigm that allows structure and representation to inform one another. We propose CFU, a novel Co-evolving Framework under Uncertainty, which reconceptualizes graph structure as a dynamic and learnable component evolving alongside node semantics. Specifically, CFU begins with a structure-aware contrastive pretraining phase to distill latent relational semantics without supervision. It then performs bidirectional structural refinement, filtering structurally redundant edges through semantic agreement scoring, and uncovering temporally contingent, task-relevant dependencies via similarity-guided inference. These operations are integrated through a dynamic fusion procedure that continuously aligns the evolving topology with the resilience objective. By embedding structural adaptation within the learning loop, CFU enables context-aware resilience assessment across incomplete, ambiguous, and structurally volatile enterprise environments. Ultimately, extensive experiments conducted on real-world datasets demonstrate its superior performance across diverse evaluation scenarios.