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.

The inherent fluctuations in the stock market present significant challenges in understanding stock dynamics, especially for investment decisions based on stock ranking. Recent advancements in learning-based methods have led to promising results in exploring temporal dependencies to understand stock movements. However, they often assume stable, certain, and reliable environments, narrowing their insight into the complex and fluctuating nature of markets. This complexity is driven by two influential factors: the explicit consistency of dynamic yet stable trends across diverse temporal patterns, coupled with the implicit interplay of logic and possibility under uncertainty. Hence, we introduce aSensitivity-awareDependencyLearning solution (SDL) for stock ranking. With bridging the ideal and reality in mind, SDL captures short-term fluctuations under the guidance of long-term dependencies, associated with the augmentation of counterfactual knowledge. Specifically, SDL devises aShort-termCo-integrationDetector (SCD) that concentrates on capturing time-varying correlations and immediate market reactions, in addition to multi-period attention. Furthermore, aLong-termCo-movementsTracker (LCT) takes advantage of enduring industry relationships and incorporates counterfactual knowledge, allowing the model to generalize beyond observed patterns and identify diverse long-term trends. Comprehensive experiments on five real-world stock markets demonstrate that our proposed SDL outperforms several representative baselines.