Explainable transaction risk analysis is a challenge for traditional deep learning models, which only predict suspicious transactions without explanations. Current explainable methods rely on hand-crafted rules and lack the ability to automatically generate language-based explanations. Large Language Models (LLMs) offer promise due to their reasoning and text generation abilities but struggle with domain knowledge and hallucinations, making risk analysis difficult. Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. Expert knowledge compensates for domain gaps, while reasoning case retrieval provides step-wise analysis guidance. We incorporate both graph-structured features and semantic features into the retrieval process to enhance the effectiveness of the retrieval. Extensive experiments show that Dual-gRAG improves LLMs' risk analysis capabilities, achieving a 15% increase in different metrics.

Due to the high similarity between camouflaged instances and the surroundings and the widespread camouflage-like scenarios, the recently proposed camouflaged instance segmentation (CIS) is a challenging and relevant task. Previous approaches achieve some progress on CIS, while many overlook camouflaged objects’ color and contour nature and then decide on each candidate instinctively. In this paper, we contribute a Mixture-of-Queries Transformer (MoQT) in an end-to-end manner for CIS based on two key designs (a Frequency Enhancement Feature Extractor and a Mixture-of-Queries Decoder). First, the Frequency Enhancement Feature Extractor is responsible for capturing the camouflaged clues in the frequency domain. To expose camouflaged instances, the extractor enhances the effectiveness of contour, eliminates the interference color, and obtains suitable features simultaneously. Second, a Mixture-of-Queries Decoder utilizes multiple newly initialized experts of queries (a group of queries considered an expert) in each layer for spotting camouflaged characteristics with cooperation. These experts collaborate to generate outputs with the mixture-of-queries mechanism, refined hierarchically to a fine-grained level for more accurate instance masks. Coupling these two components enables MoQT to use multiple experts to integrate effective clues of camouflaged objects in both spatial and frequency domains. Extensive experimental results demonstrate our MoQT outperforms 19 state-of-the-art CIS approaches on both COD10K and NC4K datasets.

Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors.

Due to the heterogeneous architectures and class skew, the global representation models training in resource-adaptive federated self-supervised learning face with tricky challenges: $\textit{deviated representation abilities}$ and $\textit{inconsistent representation spaces}$. In this work, we are the first to propose a multi-teacher knowledge distillation framework, namely $\textit{FedMKD}$, to learn global representations with whole class knowledge from heterogeneous clients even under extreme class skew. Firstly, the adaptive knowledge integration mechanism is designed to learn better representations from all heterogeneous models with deviated representation abilities. Then the weighted combination of the self-supervised loss and the distillation loss can support the global model to encode all classes from clients into a unified space. Besides, the global knowledge anchored alignment module can make the local representation spaces close to the global spaces, which further improves the representation abilities of local ones. Finally, extensive experiments conducted on two datasets demonstrate the effectiveness of $\textit{FedMKD}$ which outperforms state-of-the-art baselines 4.78\% under linear evaluation on average.

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks. Our research primarily addresses the theoretical underpinnings of similarity-based edge reconstruction attacks (SERA), furnishing a non-asymptotic analysis of their reconstruction capacities. Moreover, we present empirical corroboration indicating that such attacks can perfectly reconstruct sparse graphs as graph size increases. Conversely, we establish that sparsity is a critical factor for SERA's effectiveness, as demonstrated through analysis and experiments on (dense) stochastic block models. Finally, we explore the resilience of private graph representations produced via noisy aggregation (NAG) mechanism against SERA. Through theoretical analysis and empirical assessments, we affirm the mitigation of SERA using NAG . In parallel, we also empirically delineate instances wherein SERA demonstrates both efficacy and deficiency in its capacity to function as an instrument for elucidating the trade-off between privacy and utility.

We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models achieve predictive performance comparable to or sometimes surpassing state-of-the-art survival models. Our code is publicly availabel at https://github.com/Rorschach1989/nfm

Fraud detection in e-commerce, which is critical to protecting the capital safety of users and financial corporations, aims at determining whether an online transaction or other activity is fraudulent or not. This problem has been previously addressed by various fully supervised learning methods. However, the true labels for training a supervised fraud detection model are difficult to collect in many real-world cases. To circumvent this issue, a series of automatic annotation techniques are employed instead in generating multiple noisy annotations for each unknown activity. In order to utilize these low-quality, multi-sourced annotations in achieving reliable detection results, we propose an iterative two-staged fraud detection framework with multi-sourced extremely noisy annotations. In label aggregation stage, multi-sourced labels are integrated by voting with adaptive weights; and in label correction stage, the correctness of the aggregated labels are properly estimated with the help of a handful of exactly labeled data and the results are used to train a robust fraud detector. These two stages benefit from each other, and the iterative executions lead to steadily improved detection results. Therefore, our method is termed "Label Aggregation and Correction" (LAC). Experimentally, we collect millions of transaction records from Alipay in two different fraud detection scenarios, i.e., credit card theft and promotion abuse fraud. When compared with state-of-the-art counterparts, our method can achieve at least 0.019 and 0.117 improvements in terms of average AUC on the two collected datasets, which clearly demonstrate the effectiveness.

Typically, learning a deep classifier from massive cleanly annotated instances is effective but impractical in many real-world scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classifier. Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. Such coupled-view learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and refine the classifiers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classifier learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be filtered out by peer networks. By these strategies, our approach can finally learn a robust data classifier which less overfits to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.

Positive and Unlabeled learning (PU learning) aims to build a binary classifier where only positive and unlabeled data are available for classifier training. However, existing PU learning methods all work on a batch learning mode, which cannot deal with the online learning scenarios with sequential data. Therefore, this paper proposes a novel positive and unlabeled learning algorithm in an online training mode, which trains a classifier solely on the positive and unlabeled data arriving in a sequential order. Specifically, we adopt an unbiased estimate for the loss induced by the arriving positive or unlabeled examples at each time. Then we show that for any coming new single datum, the model can be updated independently and incrementally by gradient based online learning method. Furthermore, we extend our method to tackle the cases when more than one example is received at each time. Theoretically, we show that the proposed online PU learning method achieves low regret even though it receives sequential positive and unlabeled data. Empirically, we conduct intensive experiments on both benchmark and real-world datasets, and the results clearly demonstrate the effectiveness of the proposed method.

Community detection is an important task with many applications. However, there is no universal definition of communities, and a variety of algorithms have been proposed based on different assumptions. In this paper, we instead study the semi-supervised community detection problem where we are given several communities in a network as training data and aim to discover more communities. This setting makes it possible to learn concepts of communities from data without any prior knowledge. We propose the Seed Expansion with generative Adversarial Learning (SEAL), a framework for learning heuristics for community detection. SEAL contains a generative adversarial network, where the discriminator predicts whether a community is real or fake, and the generator generates communities that cheat the discriminator by implicitly fitting characteristics of real ones. The generator is a graph neural network specialized in sequential decision processes and gets trained by policy gradient. Moreover, a locator is proposed to avoid well-known free-rider effects by forming a dual learning task with the generator. Last but not least, a seed selector is utilized to provide promising seeds to the generator. We evaluate SEAL on 5 real-world networks and prove its effectiveness.