Cross-domain graph anomaly detection (GAD) aims to identify nodes that significantly deviate from normal patterns in unseen target domains, showing great potential in applications such as multimedia content security and financial risk control. However, existing methods often rely on semantic information trained on individual datasets, which makes it difficult to capture node commonalities across domains and limits generalization in complex multimedia environments. To address these challenges, we propose Zero-GAD, a universal Zero-shot Graph Anomaly Detection framework tailored for cross-domain scenarios. Zero-GAD leverages a novel de-semanticized strategy to train a unified detection model that can be directly applied to unseen domains without retraining or fine-tuning. The framework is built upon two key components: (1) a Global Information Unification Module, which projects graph data into the spectral domain and performs normalization to align the energy distribution in the frequency space; and (2) a Node-Neutralized Discrepancy Scoring Module that leverages the discrepancy between the original and reconstructed node representations to produce effective anomaly scores. Extensive experiments show that Zero-GAD achieves superior accuracy compared to existing models under a GAD setting.
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Multi-modal remote sensing image clustering aims to group similar pixels into the same cluster and separate dissimilar ones by leveraging the consistency and complementary information across multiple modalities, without relying on label guidance. Most existing deep learning-based methods address this task through a two-stage pipeline of feature learning followed by clustering, or adopt simple instance-level contrastive learning frameworks. In this paper, we propose an end-to-end deep multi-level contrastive clustering (DMLCC) model for multi-modal remote sensing images. The proposed DMLCC consists of three key components. Specifically, modality-specific vision encoders are initially employed to extract preliminary feature representations tailored to the characteristics of each modality. Spatial-spectral cross-modal fusion is then performed by integrating dedicated spatial and spectral feature extractors alongside a cross-modal fusion block to effectively capture and align complementary information across modalities. Finally, multi-level contrastive learning is applied to enhance feature discriminability through instance-level contrastive learning, while simultaneously promoting cluster separability via cluster-level contrastive learning. The network is trained in an end-to-end manner by integrating the three components to directly yield the clustering results. Extensive experiments on three datasets demonstrate that the proposed DMLCC outperforms state-of-the-art methods. Our code is publicly available at https://github.com/ZhangYongshan/DMLCC.
In multi-view clustering (MVC), anchor technique is generally hailed as an effective means for filtering noise and improving computation efficiency. However, existing methods usually construct anchors via heuristic strategy, random sampling, or orthogonal learning, which overlook the distribution differences between anchors and original data, leading to anchors lacking structural characteristics. To generate the anchors that are with similar distributions to original data, in the paper we carefully devise a LASD algorithm from the perspective of optimal transport (OT). Concretely, we firstly design a Multi-View OT (MVOT) framework through complementary and consensus representation learning. Then, we theoretically demonstrate the convexity of MVOT using the positive semidefiniteness of its Hessian matrix, and accordingly the global optimal solution of each transport plan can be reached. Further, we establish the strong dual condition for MVOT by the relative interior. Based on dual programming, consequently, we successfully obtain the transport plan between anchors and original data for each view within linear computational complexity. Afterwards, the spectral clustering operation is employed on the consensus plan to produce the discrete cluster labels. Abundant experiments underscore that our learned anchors do well reflect the distributions of original data, and the generated clustering results outperform multiple strong MVC competitors, even under large-scale scenarios. The source code is available at https://github.com/junpuzhang/LASD.
The RAW domain image super-resolution faces two critical challenges: the physical impossibility of capturing native high-quality RAW references with a resolution-limited camera and the limitations of neural networks, including inefficient residual layer utilization and spectral bias in feature learning. This paper proposes a strategy combining physics-based imaging simulation and neural networks to jointly address these challenges. First, we develop a rapid imaging simulation system based on our proposed subgraph decomposition technology. It generates camera-specific degraded and clean RAW image pairs at multiple resolutions. Second, we design a LatentKAN network, featuring an iterative feature fusion network that extracts additional beneficial information through stage-wise supervision and a multi-layer Kolmogorov Arnold network that suppresses spectral bias via learnable activation functions. Ultimately, our strategy demonstrates significant advantages, achieving an average 0.8 dB PSNR improvement across all SR scales compared to state-of-the-art methods, thereby establishing a new paradigm for camera-specific super-resolution tasks.
Signed graph clustering is a critical technique for discovering community structures in graphs that exhibit both positive and negative relationships. We have identified two significant challenges in this domain: i) existing signed spectral methods are highly vulnerable to noise, which is prevalent in real-world scenarios; ii) the guiding principle "an enemy of my enemy is my friend", rooted in Social Balance Theory, often narrows or disrupts cluster boundaries in mainstream signed graph neural networks. Addressing these challenges, we propose the Deep Signed Graph Clustering framework (DSGC), which leverages Weak Balance Theory to enhance preprocessing and encoding for robust representation learning. First, DSGC introduces Violation Sign-Refine to denoise the signed network by correcting noisy edges with high-order neighbor information. Subsequently, Density-based Augmentation enhances semantic structures by adding positive edges within clusters and negative edges across clusters, following Weak Balance principles. The framework then utilizes Weak Balance principles to develop clustering-oriented signed neural networks to broaden cluster boundaries by emphasizing distinctions between negatively linked nodes. Finally, DSGC optimizes clustering assignments by minimizing a regularized clustering loss. Comprehensive experiments on synthetic and real-world datasets demonstrate DSGC consistently outperforms all baselines, establishing a new benchmark in signed graph clustering.
Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches. Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph. To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss. Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudo-label with the connection to graph spectral clustering. Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework. Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo.
Rating aggregation plays a crucial role in various fields, such as product recommendations, hotel rankings, and teaching evaluations. However, traditional averaging methods can be affected by participation bias, where some raters do not participate in the rating process, leading to potential distortions. In this paper, we consider a robust rating aggregation task under the participation bias. We assume that raters may not reveal their ratings with a certain probability depending on their individual ratings, resulting in partially observed samples. Our goal is to minimize the expected squared loss between the aggregated ratings and the average of all underlying ratings (possibly unobserved) in the worst-case scenario. We focus on two settings based on whether the sample size (i.e. the number of raters) is known. In the first setting, where the sample size is known, we propose an aggregator, named as the Balanced Extremes Aggregator. It estimates unrevealed ratings with a balanced combination of extreme ratings. When the sample size is unknown, we derive another aggregator, the Polarizing-Averaging Aggregator, which becomes optimal as the sample size grows to infinity. Numerical results demonstrate the superiority of our proposed aggregators in mitigating participation bias, compared to simple averaging and the spectral method. Furthermore, we validate the effectiveness of our aggregators on a real-world dataset.
Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us. In this paper, we seek to develop margin-based generalization bounds for four representative classes of hypergraph neural networks, including convolutional-based methods (UniGCN), set-based aggregation (AllDeepSets), invariant and equivariant transformations (M-IGN), and tensor-based approaches (T-MPHN). Through the PAC-Bayes framework, our results reveal the manner in which hypergraph structure and spectral norms of the learned weights can affect the generalization bounds, where the key technical challenge lies in developing new perturbation analysis for hypergraph neural networks, which offers a rigorous understanding of how variations in the model's weights and hypergraph structure impact its generalization behavior. Our empirical study examines the relationship between the practical performance and theoretical bounds of the models over synthetic and real-world datasets. One of our primary observations is the strong correlation between the theoretical bounds and empirical loss, with statistically significant consistency in most cases.
The detection of methane emissions has drawn significant attention from researchers, as methane is one of the main contributors to global warming. Due to their high accessibility and extensive coverage, satellite-based remote sensing data have become a widely used tool for studying methane emissions. The multispectral satellite Sentinel-2 is particularly prominent for studies related to methane, as it provides shortwave infrared (SWIR) spectral band data that align with the methane absorption line. Previous studies on methane emission detection using Sentinel-2 data rely mainly on synthetic data, which involves simulated methane emission plumes overlaid on real Sentinel-2 backgrounds. However, our analysis reveals that Sentinel-2 images with simulated plumes exhibit different data distributions and visual characteristics from those of real-world data. Moreover, these studies typically use only a single reference image as a clean background along with the plume image, which we find to be insufficient. Finally, the lack of publicly available benchmarks hinders research and development in this domain. To address these challenges, we introduce the MethaneS2CM dataset for methane emission detection, collected from Sentinel-2 multispectral data and the CarbonMapper platform. Our dataset consists of two main products (L1C and L2A) of Sentinel-2, including over 4000 plumes from 43 countries, ranging from 2016 to 2024. The dataset incorporates both long-term and short-term variations in Sentinel-2 images, with each sample consisting of images captured at different time periods. Furthermore, we conduct extensive experiments to evaluate a wide range of existing methane emission detection methods and models based on this dataset. We subsequently propose a deep methane emission classification model, MEECNet. Experimental results demonstrate that MEECNet outperforms all baseline methods. Our dataset is publicly available at https://huggingface.co/datasets/H1deaki/MethaneS2CM.
Local Homophily-Aware Graph Neural Network with Adaptive Polynomial Filters for Scalable Graph Anomaly Detection
PDF ↗This paper presents the Local Homophily Graph Neural Network (LH-GNN), a novel framework for Graph Anomaly Detection (GAD). Anomalous activities in graphs often exhibit a complex interplay of homophily and heterophily, with our analysis revealing that anomalous nodes typically display a higher degree of heterophily compared to normal nodes. Existing GNN-based methods start to incorporate heterophily modeling but fail to address two critical challenges: (1) the efficiency challenge, as traditional spectral decomposition based methods are computationally expensive, and (2) the local homophily estimation challenge, where prior knowledge of node-wise homophily ratios is often unavailable. To address these challenges, LH-GNN introduces a lightweight polynomial graph filter that dynamically adjusts to node-specific homophily ratios, enabling efficient representation learning for both normal and anomalous nodes through adaptable heterophilic and homophilic bases. This design achieves linear time complexity, significantly improving computational efficiency. Additionally, we propose an iterative prototype learning strategy to estimate local homophily values without requiring additional labels. This strategy leverages class prototypes and uncertainty measures to assign reliable pseudo-labels, effectively capturing node-wise homophily. Together, these innovations enable LH-GNN to overcome the limitations of existing methods. Extensive experiments on four benchmark datasets demonstrate that LH-GNN outperforms state-of-the-art methods in both effectiveness and efficiency, achieving 4.4% improvements in detection accuracy and 11× computational speedup.
Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We empirically verify our method's scalability on multiple real-world high-dimensional datasets; our method outperforms legacy methods w.r.t. predictive performance and efficiency.
Graph neural networks (GNNs), which capture graph structures via a feature aggregation mechanism following the graph embedding framework, have demonstrated a powerful ability to support various tasks. According to the topology properties (e.g., structural roles or community memberships of nodes) to be preserved, graph embedding can be categorized into identity and position embedding. However, it is unclear for most GNN-based methods which property they can capture. Some of them may also suffer from low efficiency and scalability caused by several time- and space-consuming procedures (e.g., feature extraction and training). From a perspective of graph signal processing, we find that high- and low-frequency information in the graph spectral domain may characterize node identities and positions, respectively. Based on this investigation, we propose random feature aggregation (RFA) for efficient identity and position embedding, serving as an extreme ablation study regarding GNN feature aggregation. RFA (i) adopts a spectral-based GNN without learnable parameters as its backbone(ii) only uses random noises as inputs, and (iii) derives embeddings via just one feed-forward propagation (FFP). Inspired by degree-corrected spectral clustering, we further introduce a degree correction mechanism to the GNN backbone. Surprisingly, our experiments demonstrate that two variants of RFA with high- and low-pass filters can respectively derive informative identity and position embeddings via just one FFP (i.e., without any training). As a result, RFA can achieve a better trade-off between quality and efficiency for both identity and position embedding over various baselines. We have made our code public at https://github.com/KuroginQin/RFA
Imputation via Domain Adaptation: Rethinking Variable Subset Forecasting from Knowledge Transfer
PDF ↗Multivariate time series forecasting in practical deployment faces a critical challenge termed Variable Subset Forecasting (VSF), where certain variables accessible during training are entirely missing during inference. This creates a stark discrepancy between the training (source domain with full variables) and inference (target domain with partial variables) environments, disrupting cross-variable dependencies and fragmenting global temporal patterns. Existing imputation methods, limited to transferring local knowledge (e.g., temporal neighbors or pairwise correlations), fail to capture essential global dynamics, leading to severe performance degradation under distribution shifts. To address these challenges, we redefine VSF as a cross-domain knowledge transfer problem and propose VIDA, a framework that systematically transfers Variable Invariant knowledge from complete to partial observations through Domain Adaptation. Key to our approach is (1) Global time-frequency joint representation learning, which encodes temporal dynamics via dilated convolutions and captures low-frequency spectral consistency using Fourier neural operators, and (2) Sinkhorn-regularized distribution alignment to bridge non-overlapping feature supports across domains via optimal transport. Unlike imputation-first methods, VIDA enforces task-driven consistency by jointly optimizing predictions on reconstructed and original data, ensuring the transferred knowledge directly enhances forecasting robustness. Extensive experiments across four real-world datasets show that VIDA outperforms state-of-the-art imputation methods by 25% on average with partially observed variables. This work establishes a new paradigm for variable-missing scenarios by unifying imputation and forecasting through principled knowledge transfer.
Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters. Our code is available at https://github.com/HsipingLi/ChiGAD.
Graph Neural Networks (GNNs) have demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data format. However, when graph-structured side information (e.g., multimodal similarity graphs or social networks) is integrated into the U-I bipartite graph, existing graph collaborative filtering methods fall short of achieving satisfactory performance. We quantitatively analyze this problem from a spectral perspective. Recall that a bipartite graph possesses a full spectrum within the range of [-1, 1], with the highest frequency exactly achievable at -1 and the lowest frequency at 1; however, we observe as more side information is incorporated, the highest frequency of the augmented adjacency matrix progressively shifts rightward. This spectrum shift phenomenon has caused previous approaches built for the full spectrum [-1, 1] to assign mismatched importance to different frequencies. To this end, we propose Spectrum Shift Correction (dubbed SSC), incorporating shifting and scaling factors to enable spectral GNNs to adapt to the shifted spectrum. Unlike previous paradigms of leveraging side information, which necessitate tailored designs for diverse data types, SSC directly connects traditional graph collaborative filtering with any graph-structured side information. Experiments on social and multimodal recommendation demonstrate the effectiveness of SSC, achieving relative improvements of up to 23% without incurring any additional computational overhead. Our code is available at https://github.com/yhhe2004/SSC-KDD.
Variable Subset Forecasting (VSF) poses critical challenges in time series analysis when entire variables become unavailable during inference. Existing imputation methods relying on inter-variable correlations fail catastrophically in VSF due to two inherent limitations: (1) Missing variable collapse, where the complete absence of certain variables invalidates correlation-based dependency learning, and (2) Temporal covariate shift, where time-evolving data distributions destabilize correlation patterns learned from training data. To address these fundamental issues, we propose Generative Imputation with Multi-level Causal Consistency (GIMCC ), establishing causality-driven imputation as the first principled solution for VSF. Our key innovation lies in enforcing causal invariance through dual consistency constraints: global causal isomorphism ensures the imputed variables preserve the ground-truth causal graph structure of the complete system, while local causal subgraph alignment maintains consistency between observed variables and their causal neighborhood dependencies. By decoupling causality from spurious correlations, GIMCC provides time-invariant imputation signals robust to distribution shifts, which explicitly preserves causal relationships via multivariate spectral convolutions. Extensive experiments across five real-world domains demonstrate that GIMCC achieves average improvements of 20-60% in MAE/RMSE over correlation-based imputation baselines, remarkably outperforming full-variable training ( Oracle ) in temporal covariate shift scenarios. Our work bridges the critical gap between causal analysis and practical forecasting systems under variable absence, offering theoretically grounded guarantees for real-world deployment.
In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to capture changes in higher-order interactions that are crucial for identifying complex structural anomalies. These higher-order interactions can arise either directly from the underlying data itself or through graph lifting techniques. Our approach leverages the spectral properties of Hodge Laplacians of simplicial complexes to effectively model multi-way interactions among data points. By incorporating higher-dimensional simplicial structures into our method, our method enhances both detection accuracy and computational efficiency. Through comprehensive experiments on both synthetic and real-world datasets, we demonstrate that our approach outperforms existing graph methods in detecting both events and change points.
Graph Contrastive Learning (GCL) emerged as a powerful paradigm in self-supervised graph representation learning. While earlier applications of GCL rely on homophily assumptions, spectral graph neural networks (GNNs) enhance the effectiveness of GCL on heterophilic graphs by incorporating both low-pass and high-pass filters. However, due to numerical considerations, existing approaches oversimplify low-pass and high-pass filters by modeling them as basic linear operations, failing to capture complex topological relationships. Here, we propose ArnoldiGCL, a novel algorithm that enables the application of complex spectral filters for Graph Contrastive Learning (GCL). Using Arnoldi orthonormalization-based Chebyshev interpolation, ArnoldiGCL overcomes the difficulties posed by ill-conditioned Vandermonde systems that arise in the modeling of complex filters. By introducing learnable filters, our method generates diverse spectral views and effectively captures nuanced graph structures. Theoretical analysis demonstrates that ArnoldiGCL accurately interpolates complex filters, thus forming a solid foundation for contrastive learning on graphs with complex structures. Extensive experiments on real-world datasets confirm that ArnoldiGCL significantly outperforms state-of-the-art GCL algorithms on both homophilic and heterophilic graphs, showcasing its robustness and versatility. The code for ArnoldiGCLis available as open source at: https://github.com/mustafaCoskunAgu/ArnoldiGCL
Despite the theoretical expressiveness of 2-dimensional Folklore Weisfeiler-Lehman (2-FWL) Graph Neural Networks (GNNs), a significant gap persists between their theoretical capacity and their practical performance. To bridge this gap, we identify a critical limitation in current Graph Structural Encodings (GSEs): insufficient sensitivity to subtle structural variations, particularly in local connectivity, spectral features, and distance-based patterns. We show that widely used GSEs-such as Relative Random Walk Probability (RRWP) and monomial-based methods-lack full sensitivity across spectral frequency bands and long-range distances. Moreover, they fail to capture fine-grained local connectivity, which is essential for identifying cut nodes, biconnected components, and other higher-order structures that 2-FWL GNNs theoretically encode. To address these limitations, we propose CSDGSE (Connectivity, Spectral, and Distance Graph Structural Encoding), a novel GSE framework that jointly enhances sensitivity to: (1) exact local connectivity via hierarchical graph decomposition(2) full-frequency spectral features using expressive graph polynomials (e.g., Chebyshev), and (3) full-range distance interactions. A key innovation is our scalable divide-and-conquer algorithm for computing exact local connectivity across all node pairs, enabling efficient integration into modern GSEs. Extensive experiments show that CSDGSE outperforms existing GSEs in capturing complex structural patterns, achieving state-of-the-art results on molecular property prediction benchmarks like ZINC. Our work sets a new standard for GSEs by aligning theoretical expressiveness with practical effectiveness through enhanced structural sensitivity.
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this task, which learn point-wise representation via unsupervised learning. However, the unlabeled anomaly points in training data may cause these reconstruction-based methods to learn and reconstruct anomalous data, resulting in the challenge of capturing normal patterns. In this paper, we propose a time series anomaly detection method based on implicit neural representation (INR) reconstruction, named TSINR, to address this challenge. Due to the property of spectral bias, TSINR enables prioritizing low-frequency signals and exhibiting poorer performance on high-frequency abnormal data. Specifically, we adopt INR to parameterize time series data as a continuous function and employ a transformer-based architecture to predict the INR of given data. As a result, the proposed TSINR method achieves the advantage of capturing the temporal continuity and thus is more sensitive to discontinuous anomaly data. In addition, we further design a novel form of INR continuous function to learn inter- and intra-channel information, and leverage a pre-trained large language model to amplify the intense fluctuations in anomalies. Extensive experiments demonstrate that TSINR achieves superior overall performance on both univariate and multivariate time series anomaly detection benchmarks compared to other state-of-the-art reconstruction-based methods. Our codes are available here.