Irregular time series analysis is increasingly essential in data management due to the proliferation of complex data irregularly sampled by real-world systems. Traditional time series models, including RNN-based models and transformer variants, face significant challenges in generalizing to continuous-time paradigms, which are essential for capturing the ongoing dynamics of irregular time series. Neural Ordinary Differential Equations (NODEs) assume a continuous latent dynamic and provide an elegant framework for irregular time series analysis, yet they suffer from limitations like fragmented latent processes and the inability to fully exploit interdependencies among observations. To address these challenges, we propose a novel Differentiable hidden state enhanced neural ODE framework, termed DIFFODE, designed to effectively model irregular time series. Concretely, we introduce an attention-based differential hidden state that maps irregular observations into a continuous hidden state space, enabling the extraction of latent dynamics while preserving temporal continuity. Leveraging the theory of generalized inverses, DIFFODE innovatively derives ODEs to describe hidden state dynamics. Furthermore, we incorporate the Hoyer metric into our framework to enhance its capacity to capture subtle yet critical temporal shifts, significantly improving the accuracy of time series modeling. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of DIFFODE across three key tasks, including irregular time series classification, interpolation, and extrapolation.

While existing spatiotemporal prediction models have shown promising performance, they often rely on the assumption of input-label spatiotemporal consistency, and their high complexity raises concerns about scalability. To enhance both efficiency and performance, we integrate label information into the learning process and propose a spatiotemporal dynamic theory that outlines a bi-directional learning paradigm. Building on this paradigm, we design BiST, a lightweight yet effective Bi -directional S patio -T emporal prediction model. BiST incorporates two key processes: a forward spatiotemporal learning process and a backward correction process. The forward process utilizes MLP layers exclusively to model input correlations and generate base prediction. In the backward process, we implement a spatiotemporal decoupling module, which can learn the residual modeling deviation between input and label representations from a decoupled perspective. After smoothing the residual with a diffusion module, we can obtain the correction term to correct the base predictions. This innovative design enables BiST to achieve competitive performance while remaining lightweight. We evaluate BiST against 26 baselines across 13 datasets, including a large-scale dataset with ten thousand nodes and a longrange dataset spanning 20 years. An impressive experimental result demonstrates that BiST achieves a 8.13% improvement in performance compared to state-of-the-art models while consuming only 1.86% of the training time and 7.36% of the memory usage.

Subgraph learning has dominated most practices of improving the expressive power of Message Passing Neural Networks (MPNNs). Existing subgraph discovery policies can be classified into node-based and partition-based, which both achieve impressive performance in most scenarios. However, both mainstream solutions still face a subgraph degradation trap. Subgraph degradation is reflected in the phenomenon that the subgraph-level methods fail to offer any benefits over node-level MPNNs. In this work, we empirically investigate the existence of the subgraph degradation issue and introduce a unified perspective, perfect reconstruction, to provide insights for improving two lines of methods. We further propose a subgraph learning strategy guided by the principle of perfect reconstruction. To achieve this, two major issues should be well-addressed, i.e.,(i) how to ensure the subgraphs to possess with ‘perfect’ information? (ii) how to guarantee the ‘reconstruction’ power of obtained subgraphs?First, we propose a subgraph partition strategyRayleigh-resistanceto extract non-overlap subgraphs by leveraging the graph spectral theory. Second, we put forward aQuerymechanism to achieve subgraph-level equivariant learning, which guarantees subgraph reconstruction ability. These two parts,perfect subgraph partitionandequivariant subgraph learningare seamlessly unified as a novelRayleigh-resistanceEquivariantSubgraph learningarchitecture (RayE-Sub). Comprehensive experiments on both synthetic and real datasets demonstrate that our approach can consistently outperform previous subgraph learning architectures.

Pre-training GNNs to extract transferable knowledge and apply it to downstream tasks has become the de facto standard of graph representation learning. Recent works focused on designing self-supervised pre-training tasks to extract useful and universal transferable knowledge from large-scale unlabeled data. However, they have to face an inevitable question: traditional pre-training strategies that aim at extracting useful information about pre-training tasks, may not extract all useful information about the downstream task. In this paper, we reexamine the pre-training process within traditional pre-training and fine-tuning frameworks from the perspective of Information Bottleneck (IB) and confirm that the forgetting phenomenon in pre-training phase may cause detrimental effects on downstream tasks. Therefore, we propose a novelDelayedBottleneckingPre-training (DBP) framework which maintains as much as possible mutual information between latent representations and training data during pre-training phase by suppressing the compression operation and delays the compression operation to fine-tuning phase to make sure the compression can be guided with labeled fine-tuning data and downstream tasks. To achieve this, we design two information control objectives that can be directly optimized and further integrate them into the actual model design. Extensive experiments on both chemistry and biology domains demonstrate the effectiveness of DBP.

Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged for traffic prediction and achieved remarkable success. However, these models have limitations in terms of generalization and scalability when dealing with Out-of-Distribution (OOD) graph data with both structural and temporal shifts. To tackle the challenges of spatio-temporal shift, we propose a framework called STONE by learning invariable node dependencies, which achieve stable performance in variable environments. STONE initially employs gated-transformers to extract spatial and temporal semantic graphs. These two kinds of graphs represent spatial and temporal dependencies, respectively. Then we design three techniques to address spatio-temporal shifts. Firstly, we introduce a Fréchet embedding method that is insensitive to structural shifts, and this embedding space can integrate loose position dependencies of nodes within the graph. Secondly, we propose a graph intervention mechanism to generate multiple variant environments by perturbing two kinds of semantic graphs without any data augmentations, and STONE can explore invariant node representation from environments. Finally, we further introduce an explore-to-extrapolate risk objective to enhance the variety of generated environments. We conduct experiments on multiple traffic datasets, and the results demonstrate that our proposed model exhibits competitive performance in terms of generalization and scalability.

Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems, which empowers diverse urban services. Existing traffic forecasting frameworks usually devise various learning strategies to capture spatiotemporal correlations from the perspective of volume itself. However, we argue that previous traffic predictions are still unreliable due to two aspects. First, the influences of context factor-wise interactions on dynamic region-wise correlations are under exploitation. Second, the dynamics induce the credibility issue of forecasting that has not been well-explored. In this paper, we exploit the informative traffic-related context factors to jointly tackle the dynamic regional heterogeneity and explain the stochasticity, towards a credible uncertainty-aware traffic forecasting. Specifically, to internalize the dynamic contextual influences into learning process, we design a context-cross relational embedding to capture interactions between each context, and generate virtual graph topology to dynamically relate pairwise regions with context embedding. To quantify the prediction credibility, we attribute data-side aleatoric uncertainty to contexts and re-utilize them for aleatoric uncertainty quantification. Then we couple a dual-pipeline learning with the same objective to produce the discrepancy of model outputs and quantify model-side epistemic uncertainty. These two uncertainties are fed through a spatiotemporal network for extracting uncertainty evolution patterns. Finally, comprehensive experiments and model deployments have corroborated the credibility of our framework.

Graph Neural Networks (GNNs) can learn representative graph-level features to achieve efficient graph classification. But GNNs usually assume an environment where both class and structure distribution are balanced. Although previous works have considered the graph classification problem under the scenario of class imbalance or structure imbalance, they habitually ignored the obvious fact that class imbalance and structural imbalance are often intertwined in the real world. In this paper, we propose a carefully designed structure-driven learning framework called ImbGNN to address the potential intertwined class imbalance and structural imbalance in graph classification. Specifically, we find that feature-oriented augmentation (e.g., feature masking) and structure-oriented augmentation (e.g., edge perturbation) will have differential impacts when applied to different graphs. Therefore, we design optional augmentation based on the average degree distribution to alleviate structural imbalance. Furthermore, based on the imbalance of graph size distribution, we utilize a similarity-friendly graph random walk to extract a core subgraph to improve the accuracy of graph kernel similarity calculation, and then construct a more reasonable kernel-based graph of graphs, thereby alleviating the class imbalance and size imbalance. Extensive experiments on multiple benchmark datasets demonstrate that our proposed ImbGNN framework outperforms previous baselines on imbalanced graph classification tasks. The code of ImbGNN is available in~https://github.com/Xiaovy/ImbGNN.

Recently, spatiotemporal graph convolutional networks are becoming popular in the field of traffic flow prediction and significantly improve prediction accuracy. However, the majority of existing traffic flow prediction models are tailored to static traffic networks and fail to model the continuous evolution and expansion of traffic networks. In this work, we move to investigate the challenge of traffic flow prediction on an expanding traffic network. And we propose an efficient and effective continual learning framework to achieve continuous traffic flow prediction without the access to historical graph data, namely Pattern Expansion and Consolidation based on Pattern Matching based (PECPM). Specifically, we first design a pattern bank based on pattern matching to store representative patterns of the road network. With the expansion of the road network, the model configured with such a bank module can achieve continuous traffic prediction by effectively managing patterns stored in the bank. The core idea is to continuously update new patterns while consolidating learned ones. Specifically, we design a pattern expansion mechanism that can detect evolved and new patterns from the updated network, then these unknown patterns are expanded into the pattern bank to adapt to the updated road network. Additionally, we propose a pattern consolidation mechanism that includes both a bank preservation mechanism and a pattern traceability mechanism. This can effectively consolidate the learned patterns in the bank without requiring access to detailed historical graph data. We construct experiments on real-world traffic datasets to demonstrate the competitive performance, superior efficiency, and strong generalization ability of PECPM.

Missing values, which are common in multivariate time series, is most important obstacle towards the utilization and interpretation of those data. Great efforts have been employed on how to accurately impute missing values in multivariate time series, and existing works either use deep learning networks to achieve deterministic imputations or aim at generating different plausible imputations by sampling multiple noises from a same distribution and then denoising them. However, these models either fall short of modeling the uncertainties of imputations due to their deterministic nature or perform poorly in terms of interpretability and imputation accuracy due to their ignorance of the correlations between the latent representations of both observed and missing values which are parts of samples from a same distribution. To this end, in this paper, we explicitly take the correlations between observed and missing values into account, and theoretically re-derive the Evidence Lower BOund (ELBO) of conditional diffusion model in the scenario of multivariate time series imputation. Based on the newly derived ELBO, we further propose a novel multivariate imputation diffusion model (MIDM) which is equipped with novel noise sampling, adding and denoising mechanisms for multivariate time series imputation, and the series of newly designed technologies jointly ensure the involving of the consistency between observed and missing values. Extensive experiments on both the tasks of multivariate time series imputation and forecasting witness the superiority of our proposed MIDM model on generating conditional estimations.

Subgraph learning has received considerable attention in its capacity of interpreting important structural information for predictions. Existing subgraph learning usually exploits statistics on predefined structures e.g., node degrees, occurrence frequency, to extract subgraphs, or refine the contents via only capturing label-relevant information with node-level sampling. Given diverse subgraph patterns, and mutual independence with local correlations on graphs, current solutions on subgraph learning still have two limitations in extraction and refinement stages. 1) The universality of extracting substructure patterns across domains is still lacking, 2) node-level sampling in refinement will distort the original local topology and none explicit guidance eliminating redundant information contribute to inefficiency issue. In this paper, we propose a unified subgraph learning scheme, Poly-Pivot Graph Neural Network (P2GNN) where we designate the centric node of each subgraph as the pivot. In the extraction stage, we present a general subgraph extraction principle, i.e., Local; Asymmetry between the centric and affiliated nodes. To this end, we asymmetrically model the similarity between each pair of nodes with random walk and quantify mutual affiliations in Affinity Propagation architecture, to extract subgraph structures. In the refinement, we devise a subgraph-level exclusion regularization to squash the target-independent information by considering mutual relations across subgraphs, cooperatively preserving a support set of subgraphs and facilitating the refinement process for graph representation. Empirical experiments on diverse web and biological graphs reveal 1.1%~7.3% improvements against best baselines, and visualized case studies prove the universality and interpretability of our P2GNN.

Spatiotemporal (ST) learning has become a crucial technique for urban digitalization. Due to expansions and dynamics of cities, current spatiotemporal models are inclined to suffer distribution shifts between training and testing sets, leading to the OOD delimma. However, few studies focus on such OOD problem in temporal regressions, let alone spatiotemporal learning. Spatiotemporal data usually reveals segment-level heterogeneity within periodicity and complex spatial dependencies, posing challenges to invariance extraction. In this paper, we find that ST relations make sense for generalization and devise a Causal ST learning framework, CauSTG, which enables invariant relation transferred to OOD scenarios. Specifically, we take temporal steps as environments, and transform spatial-temporal relations into learnable parameters. To tackle heterogeneity in periodicity, we partition temporal steps into sub-environments by identifying distinctive trend patterns, enabling re-organized samples trained separately. To extract invariance within ST observations, we propose a spatiotemporal consistency learner and a hierarchical invariance explorer to jointly filter out stable relations. Our spatiotemporal learner quantifies bi-directional spatial consistency and extracts disentangled seasonal-trend patterns via trainable parameters. Further, the hierarchical invariance explorer constructs variation-based filter to achieve both local and global invariances. Experiments reveal that CauSTG can increase at most 10.26% performance against best baselines, and visualized invariant relations can well interpret the physical rationales. The appendix and codes can be available in our Github repository.

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining. Typical spatiotemporal data prediction methods usually capture spatial dependencies by directly aggregating features of local neighboring vertices in a fixed graph. However, this kind of aggregators can only capture localized correlations between vertices, and while been stacked for larger receptive field, they fall into the dilemma of over-smoothing. Additional, in temporal perspective, traditional methods focus on fixed graphs, while the correlations among vertexes can be dynamic. And time series components integrated strategies in traditional spatiotemporal learning methods can hardly handle frequently and drastically changed sequences. To overcome those limitations of existing works, in this paper, we propose a novel multi-graph based dynamic learning framework. First, a novel Dynamic Neighbor Search (DNS) mechanism is introduced to model global dynamic correlations between vertices by constructing a feature graph (FG), where the adjacency matrix is dynamically determined by DNS. Then we further alleviate the over-smoothing issue with our newly designed Adaptive Heterogeneous Representation (AHR) module. Both FG and origin graph (OG) are fed into the AHR modules and fused in our proposed Multi-graph Fusion block. Additionally, we design a Differential Vertex Representation (DVR) module which takes advantage of differential information to model temporal trends. Extensive experiments illustrate the superior forecasting performances of our proposed multi-graph based dynamic learning framework on six real-world spatiotemporal datasets from different cities and domains, and this corroborates the solid effectiveness of our proposed framework and its superior generalization ability.

Urban housing price is widely accepted as an economic indicator which is of both business and research interest in urban computing. However, due to the complex nature of influencing factors and the sparse property of transaction records, to implement such a model is still challenging. To address these challenges, in this work, we study an effective and fine-grained model for urban subregion housing price predictions. Compared to existing works, our proposal improves the forecasting granularity from city-level to mile-level, with only publicly released transaction data. We employ a feature selection mechanism to select more relevant features. Then, we propose an integrated model, JGC MMN (Joint Gated Co-attention Based Multi-modal Network), to learn all-level features and capture spatiotemporal correlations in all-time stages with a modified densely connected convolutional network as well as current ingredients and future expectations. Next, we devise a novel JGC based fusion method to better fuse the heterogeneous data of multi-stage models by considering their interactions in temporal dimension. Finally, extensive empirical studies on real datasets demonstrate the effectiveness of our proposal, and this fine-grained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations.

Modern intelligent transportation system (ITS) has greatly benefitted people's daily life. However, the chanciness and suddenness of urban anomalies may greatly restrict the trouble-free operations of ITS. To be aware of future urban anomalies and their possible influences, great efforts have been achieved on these two aspects, but comprehensive predictions of urban anomalies including the predictions of distributions and durations, are still beingless. And the spatiotemporal cascade self/mutual exciting influences among anomalies have never been considered in previous studies. In this paper, we propose a novel Anomaly Distribution and Duration Joint-Prediction (A2DJP) algorithm to simultaneously filtrate urban subregions and estimate the duration of corresponding potential anomalies in the future. To capture the spatiotemporal correlations between urban traffics and anomalies, we use a modified Graph Convolution Network and Long Short-Term Memory integrated network. To learn the cascade correlations among anomalies themselves, we devise a novel Spatiotemporal neural Hawkes Process model, which contains a Hawkes Process (HP) based GCN and HP-based LSTM to extract the anomaly-wise spatiotemporal cascading correlations. By fusing the spatiotemporal correlations between traffics and anomalies, we then simultaneously predict the distributions and durations of future anomalies. Extensive experiments on real-world datasets demonstrate that our proposed method significantly outperforms state-of-the-art solutions.

Fusing multimodal heterogeneous data plays a vital role in recognition and prediction tasks in various fields, e.g., action recognition and traffic accident forecast. Yet, there remain some key challenges, such as heterogeneous feature interaction and feature redundancies, that significantly affect the performance of multimodal fusion. To tackle these challenges, we first devise a Unified Feature Interaction Module (UFIM) in which a novel orthogonal attention component is designed to obtain fine-grained inter-modal interaction information among heterogeneous features. Then, we propose a novel Self-Correcting Transformer Module (SCTM) which employs a modified transformer to obtain the one-to-many correlation information between the current modal feature and the merged features of other modalities to alleviate the redundancy problem. Extensive experiments on four cross-domain tasks demonstrate the effectiveness and generalization ability of our proposed method.