Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a mixture of nodes with different neighbor patterns, exhibiting local node-level homophilic and heterophilic structures. However, existing works are only devoted to designing better unified HGNN backbones for node classification tasks on heterophilic and homophilic graphs simultaneously, and their analyses of HGNN performance concerning nodes are only based on the determined data distribution without exploring the effect caused by the difference of structural pattern between training and testing nodes. How to learn invariant node representations on heterophilic graphs to handle this structure difference or distribution shifts remains unexplored. In this paper, we first discuss the limitations of previous graph-based invariant learning methods in addressing the heterophilic graph structure distribution shifts from the perspective of data augmentation. Then, we propose HEI, a framework capable of generating invariant node representations through incorporating Heterophily information, the node's estimated neighbor pattern, to infer latent Environments without augmentation, which are then used for Invariant prediction. We provide detailed theoretical guarantees to clarify the reasonability of HEI. Extensive experiments on various benchmarks and backbones can also demonstrate the effectiveness and robustness of our method compared with existing state-of-the-art baselines.

Graphs serve as fundamental representations for a diverse array of complex systems, capturing intricate relationships and interactions between entities. In many real-world scenarios, graphs exhibit non-homophilous, or heterophilous, characteristics, challenging traditional graph analysis methods rooted in homophily assumptions. Recent heterophilous methods frequently struggle with noise in node attributes, which can degrade the quality of graph representations and affect downstream task performance. Common graph augmentations, while useful, often introduce bias and irrelevant noise. This paper proposes a novel method, Robust Heterophily Graph Learning via Uniformity Augmentation (RHGL-UA), which incorporates uniformity in the augmentation process through controlled random perturbations. This approach ensures a more uniform distribution of representations across different layers of the model. By adapting to data variations and learning more diverse information, RHGL-UA significantly improves performance on downstream tasks and stands out as the first practical robust heterophily graph method using representation augmentation with a theoretical guarantee. Extensive experiments demonstrate the merit of our proposed method.

Modern online platforms are increasingly employing recommendation systems to address information overload and improve user engagement. There is an evolving paradigm in this research field that recommendation network learning occurs both on the cloud and on edges with knowledge transfer in between (i.e., edge-cloud collaboration). Recent works push this filed further by enabling edge-specific context-aware adaptivity, where model parameters are updated in real-time based on incoming on-edge data. However, we argue that frequent data exchanges between the cloud and edges often lead to inefficiency and waste of communication/computation resources, as considerable parameter updates might be redundant. To investigate this problem, we introduce Intelligent Edge-Cloud Parameter Request Model (IntellectReq). IntellectReq is designed to operate on edge, evaluating the cost-benefit landscape of parameter requests with minimal computation and communication overhead. We formulate this as a novel learning task, aimed at the detection of out-of-distribution data, thereby fine-tuning adaptive communication strategies. Further, we employ statistical mapping techniques to convert real-time user behavior into a normal distribution, thereby employing multi-sample outputs to quantify the model's uncertainty and thus its generalization capabilities. Rigorous empirical validation on four widely-adopted benchmarks evaluates our approach, evidencing a marked improvement in the efficiency and generalizability of edge-cloud collaborative and dynamic recommendation systems.

Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints. To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation (CDR), which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user/item correspondence or side information and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user/item representations. The CMCDR framework employs a vector-quantized encoder–decoder architecture, enabling the disentanglement of user/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.

This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent works in unbiased recommendation have advanced the state-of-the-art with various techniques such as re-weighting, multi-task learning, and meta-learning. Despite their empirical successes, most of them lack theoretical guarantees, forming non-negligible gaps between theories and recent algorithms. In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation. We establish a close connection between unbiased recommendation and distribution shift, which shows that existing unbiased learning objectives implicitly align biased training and unbiased test distributions. Built upon this connection, we develop two generalization bounds for existing unbiased learning methods and analyze their learning behavior. Besides, as a result of the distribution shift, we further propose a principled framework, Adversarial Self-Training (AST), for unbiased recommendation. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of AST.

Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of pre-trained models when deployed on resource-constrained devices, such as improving the performance of pre-trained cloud models on smart mobiles. While quite a lot of works have investigated the data distribution shift across clouds and devices, most of them focus on model fine-tuning on personalized data for individual devices to facilitate DMG. Despite their promising, these approaches require on-device re-training, which is practically infeasible due to the overfitting problem and high time delay when performing gradient calculation on real-time data. In this paper, we argue that the computational cost brought by fine-tuning can be rather unnecessary. We consequently present a novel perspective to improving DMG without increasing computational cost, i.e., device-specific parameter generation which directly maps data distribution to parameters. Specifically, we propose an efficient Device-cloUd collaborative parametErs generaTion framework (DUET). DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud. By doing so, DUET can rehearse the device-specific model weight realizations conditioned on the personalized real-time data for an individual device. Importantly, our DUET elegantly connects the cloud and device as a “duet” collaboration, frees the DMG from fine-tuning, and enables a faster and more accurate DMG paradigm. We conduct an extensive experimental study of DUET on three public datasets, and the experimental results confirm our framework’s effectiveness and generalisability for different DMG tasks.

The logged feedback for training recommender systems is usually subject to selection bias, which could not reflect real user preference. Thus, many efforts have been made to learn the de-biased recommender system from biased feedback. However, existing methods for dealing with selection bias are usually affected by the error of propensity weight estimation, have high variance, or assume access to uniform data, which is expensive to be collected in practice. In this work, we address these issues by proposing Learning De-biased Representations (LDR), a framework derived from the representation learning perspective. LDR bridges the gap between propensity weight estimation (WE) and unbiased weighted learning (WL) and provides an end-to-end solution that iteratively conducts WE and WL. We show LDR can effectively alleviate selection bias with bounded variance. We also perform theoretical analysis on the statistical properties of LDR, such as its bias, variance, and generalization performance. Extensive experiments on both semi-synthetic and real-world datasets demonstrate the effectiveness of LDR.

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks on graphs, which become favorable comparing with other approaches. However, similar to other machine learning models, GNN s might suffer from the bias issue because of the distribution shift between training and testing node distributions. More importantly, the test node distribution in the graph is generally unknown during model training in practice. In this paper, we focus on how to address the bias issue on graphs and learn a graph neural network model that is robust to arbitrary unknown distribution shifts. To address this problem, we propose a novel Bias-Aware Graph Neural Network (BA-GNN) framework by learning node representations that are invariant across different distributions for invariant prediction. Specifically, our BA-GNN framework contains two interactive parts, one for bias identification and the other for invariant prediction. To learn invariant feature and aggregated representation, our BA-GNN learns multiple biased graph partitions and selects feature, neighbor, and propagation steps for nodes under multiple biased graph partitions. Extensive experiments show that our proposed BA-G NN framework can significantly improve different GNNs backbones such as GCN, GAT, APPNP and GraphSAGE on different datasets.

This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the propagation strategy. For instance, the choices of propagation steps are often specialized to a single graph and are not personalized to different nodes. To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs. We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters in a variational Expectation- Maximization (VEM) framework. Extensive experiments on various types of graph benchmarks demonstrate that our proposed frame- work can significantly achieve better performance compared with the state-of-the-art methods, and can effectively learn personalized and interpretable propagate strategies of messages in GNNs.

Matrix factorization is one of the most successful methods for single-criterion recommender systems but not for multi-criteria recommender systems that contain multiple criterion-specific ratings. Tensor factorization methods have been developed to learn predictive models in multi-criteria recommender systems by dealing with the three-dimensional (3D) user-item-criterion ratings. However, they suffer from the data sparsity issues in real applications. In order to alleviate this problem, we propose deep tensor factorization (DTF) in this paper by integrating deep representation learning and tensor factorization, where the side information is embedded to provide an effective compensation for tensor sparsity. We exhibit two DTF instantiations by combining stacked denoising autoencoder (SDAE) and CANDECOMP/PARAFAC (CP) tensor factorization. Experimental results demonstrate that our DTF schemes outperform state-of-the-art methods in terms of multi-criteria recommendations.