Explainability in Graph Neural Networks (GNNs) has shown considerable promise in bolstering their trustworthiness, credibility and transparency. Our research delves into the assessment of explainability within GNNs, a pivotal factor for ensuring the reliability of explainability techniques in real-world applications. Existing evaluation metrics, typically involving taking explanatory subgraphs as inputs and measuring output differences, often face out-of-distribution (OOD) challenges. This issue occurs when explanatory subgraphs do not align with real-world data distributions, affecting the reliability of model explanations. With this in mind, in this work, we endeavor to confront this issue by introducing a novel evaluation metric, termed OOD-resistant Adversarial Robustness (OAR). Specifically, our approach is inspired by adversarial robustness, assessing the resilience of explanation subgraphs to attacks. Additionally, we incorporate a sophisticated OOD reweighting mechanism within the evaluation framework to ensure that assessments remain aligned with the original data distribution. Going beyond this, to accommodate a wider range of evaluation tasks, we further devise a counterfactual attack module and complement the perturbed subgraph using the conditional graph diffusion model. The refined paradigm, termed OAR+, ensures that our metric is versatile and applicable across various contexts. Furthermore, we establish a standardized framework, which serves as a benchmark for evaluating the fairness and accuracy of different metrics. We conduct extensive experiments to validate the effectiveness of the OAR and OAR+.

Spatio-temporal (ST) prediction is crucial in earth sciences, including meteorological forecasting and urban computing, to name just a few. Access to ample high-quality data, combined with deep models adept at inference, is essential for attaining significant outcomes. Yet, data scarcity and the substantial costs of sensor deployment result in notable data imbalances. Overly specialized models that lack causal linkages further undermine the generalizability of inference techniques. To address these challenges, we first introduce a causal framework for ST predictions, named $\mathtt{NuwaDynamics}$NuwaDynamicsNuwaDynamics, aimed at pinpointing causal regions in data and providing models with the capability for causal reasoning in a dual-phase process. Initially, we employ upstream self-supervision to identify causally significant patches, equipping the model with generalizable insights and performing targeted interventions on non-essential patches to approximate potential testing distributions. This stage is known as the discovery phase. Progressing from discovery, we apply the insights to downstream tasks tailored to specific ST goals, enhancing the model's recognition of a wider potential data distribution and augmenting its causal perceptual abilities (referred to as the Update phase). Additionally, we address environmental controllability and high computational complexity by implementing channel multiplication and conditional generation methods. This process, termed $\mathtt{NuwaDynamics+}$NuwaDynamics+NuwaDynamics+, can further be interpreted as the front-door adjustment technique in the causality domain. Through comprehensive experiments across ten real-world or simulated ST benchmarks, we demonstrate that integrating the $\mathtt{NuwaDynamics+}$NuwaDynamics+NuwaDynamics+ concept substantially improves various model performance. $\mathtt{NuwaDynamics+}$NuwaDynamics+NuwaDynamics+ concept also significantly enhances the versatility across various dynamic ST tasks, such as extreme weather forecasting and long-temporal-step super-resolution predictions.

Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of data and the high costs associated with deploying sensors lead to significant data imbalances. Models that are overly tailored and lack causal relationships further compromise the generalizabilities of inference methods. Towards this end, we first establish a causal concept for ST predictions, named NuwaDynamics, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Concretely, we initially leverage upstream self-supervision to discern causal important patches, imbuing the model with generalized information and conducting informed interventions on complementary trivial patches to extrapolate potential test distributions. This phase is referred to as the discovery step. Advancing beyond discovery step, we transfer the data to downstream tasks for targeted ST objectives, aiding the model in recognizing a broader potential distribution and fostering its causal perceptual capabilities (refer as Update step). Our concept aligns seamlessly with the contemporary backdoor adjustment mechanism in causality theory. Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the NuwaDynamics concept. NuwaDynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions.

In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames. Traditional numerical simulation systems depend largely on the initial settings and correctness of the constructed partial differential equations (PDEs). Despite recent efforts yielding significant success in discovering data-driven PDEs with neural networks, the limitations posed by singular scenarios and the absence of local insights prevent them from performing effectively in a broader real-world context. To this end, this paper propose the universal expert module – that is, optical flow estimation component, to capture the evolution laws of general physical processes in a data-driven fashion. To enhance local insight, we painstakingly design a finer-grained physical pipeline, since local characteristics may be influenced by various internal contextual information, which may contradict the macroscopic properties of the whole system. Further, we harness currently popular neural discrete learning to unveil the underlying important features in its latent space, this process better injects interpretability, which can help us obtain a powerful prior over these discrete random variables. We conduct extensive experiments and ablations to demonstrate that the proposed framework achieves large performance margins, compared with the existing SOTA baselines.