Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the generalizability of GNNs on out-of-distribution (OOD) test data. Existing methods that target learning an invariant (feature, structure)-label mapping often depend on oversimplified assumptions about the data generation process, which do not adequately reflect the actual dynamics of distribution shifts in graphs. In this paper, we introduce a more realistic graph data generation model using Structural Causal Models (SCMs), allowing us to redefine distribution shifts by pinpointing their origins within the generation process. Building on this, we propose a casual decoupling framework, DeCaf, that independently learns unbiased feature-label and structure-label mappings. We provide a detailed theoretical framework that shows how our approach can effectively mitigate the impact of various distribution shifts. We evaluate DeCaf across both real-world and synthetic datasets that demonstrate different patterns of shifts, confirming its efficacy in enhancing the generalizability of GNNs. Our code is available at: \url{https://github.com/hanxiaoxue114/DeCaf-GraphOOD.}

The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to predict patient health outcomes. However, single-visit records of patients are often neglected in the training process due to the lack of annotations of next-visit information, thereby limiting the predictive and expressive power of machine learning models. In this paper, we present a novel framework MPLite that utilizes Multi-aspect Pretraining with Lab results through a light-weight neural network to enhance medical concept representation and predict future health outcomes of individuals. By incorporating both structured medical data and additional information from lab results, our approach fully leverages patient admission records. We design a pretraining module that predicts medical codes based on lab results, ensuring robust prediction by fusing multiple aspects of features. Our experimental evaluation using both MIMIC-III and MIMIC-IV datasets demonstrates improvements over existing models in diagnosis prediction and heart failure prediction tasks, achieving a higher weighted-F1and recall with MPLite. This work reveals the potential of integrating diverse aspects of data to advance predictive modeling in healthcare.

Graph Neural Networks (GNNs) experience "catastrophic forgetting" in continual learning setups, where they tend to lose previously acquired knowledge and perform poorly on old tasks. Rehearsal-based methods, which consolidate old knowledge with a replay memory buffer, are a de facto solution due to their straightforward workflow. However, these methods often fail to adequately capture topological information, leading to incorrect input-label mappings in replay samples. To address this, we propose TACO, a topology-aware graph coarsening and continual learning framework that stores information from previous tasks as a reduced graph. Throughout each learning period, this reduced graph expands by integrating with a new graph and aligning shared nodes, followed by a "zoom-out" reduction process to maintain a stable size. We have developed a graph coarsening algorithm based on node representation proximities to efficiently reduce a graph while preserving essential topological information. We empirically demonstrate that the learning process on the reduced graph can closely approximate that on the original graph. We compare TACO with a wide range of state-of-the-art baselines, proving its superiority and the necessity of preserving high-quality topological information for effective replaying.

When working with forecasting the future, it is all about learning from the past. However, it is non-trivial to model the past due to the scale and complexity of available data. Recently, Graph Neural Networks (GNNs) have shown flexibility to process different forms of data and learn interactions among entities, giving them advantages in real-life applications. More and more researchers have started to apply GNNs and temporal models for event forecasting because events are formalized in knowledge graphs. However, most of these models are based on the Markov assumption that the probability of a event is only influenced by the state of its last time step (or recent history). We claim that the occurrence of an event not only has short-term but also long-term dependencies. In this work, we propose a temporal knowledge graph (KG)-based model that considers different granularties of histories when forecasting an event; this method also integrates news texts as auxiliary features during the graph learning process. Extensive experiments on multiple datasets are conducted to examine the effectiveness of the proposed method. Code is available at: https://github.com/yuening-lab/MTG.