The out-of-distribution (OOD) generalization of graph neural networks poses significant challenges in Web applications, where data resides in complex heterogeneous information networks. Such networks exhibit not only structural heterogeneity but also distribution shifts arising from evolving user behaviors and data collection biases. Conventional GNNs often struggle to identify stable patterns in such heterogeneous graphs, particularly in the absence of explicit environment labels. The core issue is that latent environments can create spurious correlations between node features, local topology, and labels. Models may then rely on these environment-specific shortcuts for predictions, failing to learn the invariant mechanisms that generalize under distribution shifts. To address these limitations, we propose InvHG (Invariant Learning on Heterogeneous Graphs via Subgraph Environment Inference), a causality-inspired framework that infers latent environments at the subgraph level, disentangles type-specific confounding effects, and leverages regularized expert fusion to learn invariant representations. Extensive experiments on heterogeneous graph OOD benchmarks demonstrate that InvHG consistently outperforms state-of-the-art methods, offering a robust solution for complex Web graph learning. The source code is available at https://github.com/mok630/InvHG.

To provide actionable insights and interpretations for individuals affected by algorithmic decisions, algorithmic recourse-demonstrating how outcomes change with modifications to input features-is introduced to facilitate outcome adjustment.However, existing studies often focus on different notions of feasibility and impose complex optimization constraints, relying on strong assumptions and expert knowledge that may be impractical or not widely applicable.In this paper, we propose leveraging adherence to the perturbation data manifold to model typical feasibility challenges, providing both a theoretical clarification and a practical framework.We design optimization constraints based on this model and introduce our method, the Perturbation Data Manifold Constraint (PDMC), to ensure the feasibility of generated algorithmic recourses.Through extensive experiments on both simulated and real clinical data, we validate the rationale and effectiveness of PDMC.

Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination . This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework.

Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation systems. It reflects the differences in the properties among sub-populations. Ignoring the heterogeneity in recommendation data could mislead the models, hurt the sub-populational robustness, and finally limit the performance of recommendation models. However, data heterogeneity has not received substantial attention within the recommendation community, prompting us to adequately explore and exploit data heterogeneity to solve these challenges and enhance data analysis. In this study, we specifically focus on two representative categories of heterogeneity in recommendation data: heterogeneity of prediction mechanism and covariate distribution. To explore the data heterogeneity, we propose an algorithm based on bilevel clustering. Additionally, we demonstrate how the explored data heterogeneity can be exploited for prediction and debias in recommendation scenarios, specifically by building models using multiple sub-models and augmenting the propensity score estimation. Extensive experiments conducted on real-world data substantiate the existence of heterogeneity in recommendation data and validate the effectiveness of exploring and exploiting data heterogeneity in improving recommendation performance.

Recommendation systems are frequently challenged by pervasive biases in the training set that can compromise model effectiveness. To address this issue, various debiasing techniques have been developed to eliminate biases and produce debiased models. However, when encountering varying test environments, some data patterns manifested by the training data could be beneficial to the model’s performance. Completely removing biases may overlook the beneficial data patterns and consequently diminish recommendation accuracy. Thus, it is crucial to carefully integrate certain biases to optimize performance, while the ideal level of bias integration is highly dependent on the test environment. Moreover, these systems operate in dynamic scenarios where the test environments could vary, necessitating an adaptive integration strategy customized to the environment. Our research establishes that discrepancies in predictions of models can guide the selection of the most fitting model for specific situations. Building on this understanding, we present AdaptSel, a pioneering method for the adaptive selection of the superior model during the testing phase. Empirical evaluations substantiate the foundational assumptions of AdaptSel, accentuating its effectiveness in adaptively selecting the most suitable model for varying test environments.

Despite the encouraging successes in numerous applications, machine learning methods grounded on the i.i.d. assumption often experience performance deterioration when confronted with the distribution shift between training and test data. This challenge has instigated recent research endeavors focusing on out-of-distribution (OOD) generalization. A particularly pervasive and intricate OOD problem is to enhance the model's generalization ability by training it on samples drawn from a single environment. In response to the problem, we propose a simple model-agnostic method tailored for a practical OOD scenario in this paper. Our approach centers on pursuing robust weighted empirical risks, utilizing randomly shifted training distributions derived through a specific sample-based weighting strategy. Furthermore, we theoretically establish that the expected risk of the shifted training distribution can bound the expected risk of the test distribution. This theoretical foundation ensures the improved prediction performance of our method when employed in uncertain test distributions. Extensive experiments conducted on diverse real-world datasets affirm the effectiveness of our method, highlighting its potential to address the distribution shifts in machine learning applications.

Current recommender systems have achieved great successes in online services, such as E-commerce and social media. However, they still suffer from the performance degradation in real scenarios, because various biases always occur in the generation process of user behaviors. Despite the recent development of addressing some specific type of bias, a variety of data bias, some of which are even unknown, are often mixed up in real applications. Although the uniform (or unbiased) data may help for the purpose of general debiasing, such data can either be hardly available or induce high experimental cost. In this paper, we consider a more practical setting where we aim to conduct general debiasing with the biased observational data alone. We assume that the observational user behaviors are determined by invariant preference (i.e. a user's true preference) and the variant preference (affected by some unobserved confounders). We propose a novel recommendation framework called InvPref which iteratively decomposes the invariant preference and variant preference from biased observational user behaviors by estimating heterogeneous environments corresponding to different types of latent bias. Extensive experiments, including the settings of general debiasing and specific debiasing, verify the advantages of our method.

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.

Discovering causal structure among a set of variables is a crucial task in various scientific and industrial scenarios. Given finite i.i.d. samples from a joint distribution, causal discovery is a challenging combinatorial problem in nature. The recent development in functional causal models, especially the NOTEARS provides a differentiable optimization framework for causal discovery. They formulate the structure learning problem as a task of maximum likelihood estimation over observational data (i.e., variable reconstruction) with specified structural constraints such as acyclicity and sparsity. Despite its success in terms of scalability, we find that optimizing the objectives of these differentiable methods is not always consistent with the correctness of learned causal graph especially when the variables carry heterogeneous noises (i.e., different noise types and noise variances) in real data from wild environments. In this paper, we provide the justification that their proneness to erroneous structures is mainly caused by the over-reconstruction problem, i.e., the noises of variables are absorbed into the variable reconstruction process, leading to the dependency among variable reconstruction residuals, and thus raise structure identifiability problems according to FCM theories. To remedy this, we propose a novel differentiable method DARING by imposing explicit residual independence constraint in an adversarial way. Extensive experimental results on both simulation and real data show that our proposed method is insensitive to the heterogeneity of external noise, and thus can significantly improve the causal discovery performances.

Matching is almost the first and most fundamental step in recommender systems, that is to quickly select hundreds or thousands of related entities from the whole commodity pool. Among all the matching methods, item-to-item (I2I) graph based matching is a handy and highly effective approach and is widely used in most applications, owing to the essential relationships of entities described in a powerful I2I graph. Yet, the I2I graph is not a ready-made product in a data source. To obtain it from users' behaviors, a common practice in the industry is to construct the graph based on the similarity of item embeddings or co-occurrence frequency directly. However, these methods tend to lose the complicated correlations (high-ordered or nonlinear) inside decision-making actions and cannot achieve the global optimal solution. Moreover, the correlations between items are usually contained in users' short-term actions, which are full of noise information (e.g. spurious association, missing connection). It is vitally important to filter out noise while generating the graph. In this paper, we propose a novel framework called Purified Graph Generation (PGG) dedicated to learn faithful I2I graph from sparse and noisy behavior data. We capture the 'confidence value' between user and item to get rid of exception action during decision making, and leverage it to re-sample purified sets that are fed into an unsupervised I2I graph structure learning framework called GPBG. Extensive experimental results from both simulation and real data demonstrate that our method could significantly benefit the performance of I2I graph compared to the typical baselines.

Nowadays graph has become a general and powerful representation to describe the rich relationships among different kinds of entities via the underlying patterns encoded in its structure. The knowledge (more generally) accumulated in graph is expected to be able to cross populations from one to another and the past to future. However the data collection process of graph generation is full of known or unknown sample selection biases, leading to spurious correlations among entities, especially in the non-stationary and heterogeneous environments. In this paper, we target the problem of learning stable graphs from multiple environments with selection bias. We purpose a Stable Graph Learning (SGL) framework to learn a graph that can capture general relational patterns which are irrelevant with the selection bias in an unsupervised way. Extensive experimental results from both simulation and real data demonstrate that our method could significantly benefit the generalization capacity of graph structure.