Graph anomaly detection (GAD) has recently gained significant attention due to its broad applicability across various graph-based domains. Graph contrastive learning (GCL) has emerged as a key approach for addressing GAD challenges, particularly in the absence of labeled data. However, existing GCL methods for GAD primarily rely on data augmentation for multi-hierarchy contrastive learning, often overlooking the semantic information from high-order neighbors, which results in incomplete node representations. Furthermore, GCL-based approaches frequently prioritize node attributes while neglecting the topological structure of complex graphs. To address these limitations, we propose a novel multi-hierarchy contrastive learning graph anomaly detection with structure-awareness (MCLASt) framework. Our approach first generates two augmented views by sampling neighbors from different orders to capture rich semantic information and leverages graph convolutional networks to obtain latent node embeddings. We then introduce three hierarchical contrastive learning modules: node-node, node-subgraph, and subgraph-subgraph level, to capture multi-hierarchy consistency information across nodes with varying orders of neighbors. To further enhance feature discrimination, we incorporate a reconstruction module that preserves the essential characteristics of the original node features. Finally, we propose a multi-hierarchy anomaly inference mechanism that integrates both attribute and topological anomaly signals for more accurate anomaly detection. Extensive experiments conducted on five real-world datasets demonstrate the effectiveness and advancement of the proposed MCLASt. Our code is available at https://github.com/shaieesss/MCLASt .

The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.

Temporal Knowledge Graph (TKG) reasoning aims to infer missing facts by leveraging abundant historical information, highlighting the dynamic interactions between entities and relations over time. However, existing methods often overlook potential correlations among relations and face difficulties in predicting previously unseen events. To address these challenges, we propose a novel framework, Time-aware Fact Diffusion with Contrastive Learning for Temporal Knowledge Reasoning (TFDCL), to improve TKG completion. Specifically, TFDCL incorporates a relation-guided filtering mechanism to enhance structural modeling when capturing both short-term and long-term historical features. Moreover, a Time-aware Fact Diffusion module is introduced, which injects noise into fact-level representations and progressively denoises them, thereby improving the model’s generalization to unseen events. Additionally, a contrastive learning objective is employed to align short-term and long-term representations, encouraging semantically similar events to be closer in the embedding space and better capturing the dynamic evolution of knowledge graphs. Extensive experiments on four benchmark datasets demonstrate that TFDCL consistently outperforms state-of-the-art baselines across multiple evaluation metrics, confirming its effectiveness and robustness.

Long-term Time Series Forecasting (LTSF) is an important task with extensive applications across diverse domains. While contemporary methodologies have achieved notable results through the integration of time and frequency domain features, significant challenges persist. Current approaches frequently disregard the information degradation inherent in Fast fourier transform (FFT) and inverse Fast fourier transform (IFFT) operations, substantially compromising predictive accuracy. Furthermore, conventional weighting mechanisms demonstrate limitations in their capacity to capture the intricate relationships between temporal and frequency representations, leading to suboptimal feature fusion and consequent information loss. To address these limitations, we present the Joint Time-Frequency Method (JTFM), a novel framework that simultaneously extracts sequence features from both temporal and frequency domains, thereby transcending single-domain constraints and enhancing feature comprehensiveness. Additionally, we introduce the Dynamic Harmonic Accumulation Weighting Mechanism (DHAWM), which surpasses traditional weighting approaches by dynamically modulating the relative contributions of temporal and frequency domain features based on sequence-specific characteristics. This adaptive mechanism strengthens the model’s feature representation capabilities and enhances forecasting precision. Empirical validation on eight real-world datasets demonstrates the JTFM’s superior performance compared to state-of-the-art baseline methods, establishing its efficacy in long-term time series forecasting applications.

Time series anomaly detection holds notable importance for risk identification and fault detection across diverse application domains. Unsupervised learning methods have become popular because they have no requirement for labels. However, due to the challenges posed by the multiplicity of abnormal patterns, the sparsity of anomalies, and the growth of data scale and complexity, these methods often fail to capture robust and representative dependencies within the time series for identifying anomalies. To enhance the ability of models to capture normal patterns of time series and avoid the retrogression of modeling ability triggered by the dependencies on high-quality prior knowledge, we propose a differencing-based contrastive representation learning framework for time series anomaly detection (DConAD). Specifically, DConAD generates differential data to provide additional information about time series and utilizes transformer-based architecture to capture spatiotemporal dependencies, which enhances the robustness of unbiased representation learning ability. Furthermore, DConAD implements a novel KL divergence-based contrastive learning paradigm that only uses positive samples to avoid deviation from reconstruction and deploys the stop-gradient strategy to compel convergence. Extensive experiments on five public datasets show the superiority and effectiveness of DConAD compared with nine baselines. The code is available at https://github.com/shaieesss/DConAD.

Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task. Spatial Graph Neural Networks (GNNs) have been successfully applied to fraud detection tasks due to their strong inductive learning capabilities. However, existing spatial GNN-based methods often enhance the graph structure by excluding heterophilic neighbors during message passing to align with the homophilic bias of GNNs. Unfortunately, this approach can disrupt the original graph topology and increase uncertainty in predictions. To address these limitations, this paper proposes a novel framework, Dual-channel Heterophilic Message Passing (DHMP), for fraud detection. DHMP leverages a heterophily separation module to divide the graph into homophilic and heterophilic subgraphs, mitigating the low-pass inductive bias of traditional GNNs. It then applies shared weights to capture signals at different frequencies independently and incorporates a customized sampling strategy for training. This allows nodes to adaptively balance the contributions of various signals based on their labels. Extensive experiments on three real-world datasets demonstrate that DHMP outperforms existing methods, highlighting the importance of separating signals with different frequencies for improved fraud detection. The code is available at https://github.com/shaieesss/DHMP.

Remote sensing change detection tracks surface transformations over time, aiding in disaster early warning and urban change monitoring. However, current methods struggle with challenges such as complex semantic interpretation, sensor variations, seasonal changes, and suboptimal model designs, which hinder accurate detection. To overcome these issues, we propose a method combining semantic enhancement and efficient temporal cross-perception. First, we enhance relational semantics by visualizing features at various levels. Shallow features are enhanced using a reversible method, while deep features are strengthened through a graph-structured non-Euclidean approach to capture global relationships. Second, we introduce an efficient bi-temporal interaction method that uses spatial matrix compression and matrix multiplication (similar to "QKV") for cross-temporal understanding, focusing on temporal changes. Finally, to address insufficient weight learning in distant decoder layers, we apply weight normalization transfer from better-understood layers, improving understanding in the generator. Our method outperforms baseline approaches, with 3.44% IoU improvement on CLCD and 1.23% IoU improvement on Google.

Abstract The core of multi‐view clustering is how to exploit the shared and specific information of multi‐view data properly. The data missing and incompleteness bring great challenges to multi‐view clustering. In this paper, we propose an innovative multi‐view subspace clustering method with incomplete graph information, so‐called incomplete multiple graphs clustering. Specifically, we creatively separate one shared and multiple specific graphs from multiple raw graph data, and exploit the mask fusion strategy and block diagonal regulariser to obtain the inherent category information. To handle the incomplete multiple graph data, we utilise multiple indicator matrices to mark the missing elements existed in each raw graph. In addition, the weight of each raw graph is adaptively learnt according to the graph importance. The alternative direction optimization algorithm is employed to solve our proposed methods. Finally, we also analyse the algorithm convergence and the computation complexity in detail. The clustering results on six real‐world datasets show that our method obviously outperforms a serious of classic incomplete multi‐view clustering methods.

Graph Neural Networks(GNNs) play a significant role and widely applied in fraud detection tasks, exhibiting significant advancements in detection performance compared to conventional methodologies. However, within the intricate structure of fraud graphs, fraudsters usually camouflage themselves among a large number of benign entities. An effective solution to address the camouflage problem involves the incorporation of complex and abundant edge information. However, existing GNNbased methods often overlook the integration of such crucial information into the message passing process, thereby limiting their efficacy. To address the above issues, this study proposes a novel Gated Edge-augmented Graph Neural Network(GE-GNN). Our approach begins with an edge-based feature augmentation mechanism that utilizes both node and edge features within a single relation. Subsequently, we apply augmented representation to the message passing process to update the node embeddings. Furthermore, we design a gate logistic to regulate the expression of augmented information. Finally, we fuse the node features across different relations to obtain a comprehensive representation. Extensive experimental results on two real-world datasets demonstrate the proposed method achieves higher performance over several state-of-the-art methods. Our code is available at https://github.com/shaieesss/GE-GNN

Abstract In this paper, we present a hybrid approach for credit scoring, and the classification performance of this approach is compared with 4 base learners in machine learning. A large credit default swap dataset covering the period from 2006 to 2016 is used to build classifiers and test their performances. The results from this empirical study indicate that the bagging ensemble method can substantially improve individual base learners such as decision tree, multilayer perceptron, and k‐nearest neighbours. The performance of support vector machine does not change after applying bagging ensemble. The overall results demonstrate that k‐nearest neighbour is more suitable than any other method when dealing with large unbalanced datasets in credit scoring.

Abstract This study proposes a novel multi‐view soft block diagonal representation framework for clustering complete and incomplete multi‐view data. First, given that the multi‐view self‐representation model offers better performance in exploring the intrinsic structure of multi‐view data, it can be nicely adopted to individually construct a graph for each view. Second, since an ideal block diagonal graph is beneficial for clustering, a ‘soft’ block diagonal affinity matrix is constructed by fusing multiple previous graphs. The soft diagonal block regulariser encourages a matrix to approximately have (not exactly) diagonal blocks, where is the number of clusters. This strategy adds robustness to noise and outliers. Third, to handle incomplete multi‐view data, multiple indicator matrices are utilised, which can mark the position of missing elements of each view. Finally, the alternative direction of multipliers algorithm is employed to optimise the proposed model, and the corresponding algorithm complexity and convergence are also analysed. Extensive experimental results on several real‐world datasets achieve the best performance among the state‐of‐the‐art complete and incomplete clustering methods, which proves the effectiveness of the proposed methods.

The outputs of upstream individual processes (members) become the inputs of downstream members in supply chains. When multiple inputs and outputs are present, data envelopment analysis has been widely applied to assess efficiency. In cooperative groups, such as supply chains, a maximin decision approach can reflect not only overall system efficiency, but also efficiency of system elements. This paper discusses a maximin efficiency multistage supply chain model capable of measuring supply chain members performance as well as overall supply chain performance.

Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.