Most self-supervised graph learning studies typically follow an offline training paradigm, assuming that all data are readily available.This assumption, however, is not always tenable in real-world scenarios as many graph data are generated continuously.Although several continual graph learning models have emerged and achieved empirical success, they almost all rely on external supervision, making it difficult to adapt to applications with a large amount of unlabeled data from the wild.To be honest, research on self-supervised continual graph learning is still surprisingly in its infancy.Therefore, we select several well-known self-supervised graph embedding models as representatives and explore whether they are resistant to catastrophic forgetting in a continual learning setting.Empirical studies find that self-supervised representation models may be potentially better continual learners than supervised counterparts.Driven by this advantage, we propose a self-supervised continual graph representation learning framework based on adaptive spaced replay on node proxies, named Trace.Inspired by the Complementary Learning System theory, Trace employs a dual-system architecture to simulate the functionality and cooperation of the hippocampus and neocortex in the brain.Among them, the fastlearning system efficiently encodes the current input graph to acquire new knowledge and adaptively extracts node proxies from it as important knowledge cached into the memory through progressive clustering.Drawing inspiration from the Ebbinghaus forgetting curve, the slow-learning system implements adaptive spaced replay based on the memory retention rate of each preceding task instead of the widely used consecutive replay scheme for promising flexibility and efficiency.Experiments under task-incremental and class-incremental learning settings on multiple datasets corroborate

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What's more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack.

In label-noise learning, accurately identifying the transition matrix is crucial for developing statistically consistent classifiers. This task is complicated by instance-dependent noise, which introduces identifiability challenges in the absence of stringent assumptions. Existing methods use neural networks to estimate the transition matrix by initially extracting confident clean instances. However, this extraction process is hindered by severe inter-class imbalance and a bias toward selecting unambiguous intra-class instances, leading to a distorted understanding of noise patterns. To tackle these challenges, our paper introduces a Class Rebalance and Geometric Regularization-based Framework (CRGR). CRGR employs a smoothed, noise-tolerant reweighting mechanism to equilibrate inter-class representation, thereby mitigating the risk of model overfitting to dominant classes. Additionally, recognizing that instances with similar characteristics often exhibit parallel noise patterns, we propose that the transition matrix should mirror the similarity of the feature space. This insight promotes the inclusion of ambiguous instances in training, serving as a form of geometric regularization. Such a strategy enhances the model's ability to navigate diverse noise patterns and strengthens its generalization capabilities. By addressing both inter-class and intra-class biases, CRGR offers a more balanced and robust classification model. Extensive experiments on both synthetic and real-world datasets demonstrate CRGR's superiority over existing state-of-the-art methods, significantly boosting classification accuracy and showcasing its effectiveness in handling instance-dependent noise.

Sentiment Analysis (SA) is a fundamental task in natural language processing, which is widely used in public decision-making. Recently, deep learning have demonstrated great potential to deal with this task. However, prior works have mostly treated SA as a deterministic classification problem, and meanwhile, without quantifying the predictive uncertainty. This presents a serious problem in the SA, different annotator, due to the differences in beliefs, values, and experiences, may have different perspectives on how to label the text sentiment. Such situation will lead to inevitable data uncertainty and make the deterministic classification models feel puzzle to make decision. To address this issue, we propose a new SA paradigm with the consideration of uncertainty and conduct an expensive empirical study. Specifically, we treat SA as the regression task and introduce uncertainty quantification to obtain confidence intervals for predictions, which enables the risk assessment ability of the model and can improve the credibility of SA-aids decision-making. Experiments on five datasets show that our proposed new paradigm effectively quantifies uncertainty in SA while remaining competitive performance to point estimation, in addition to being capable of Out-Of-Distribution~(OOD) detection.

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://github.com/yimingxu24/CLDG.

Tax evasion is an illegal activity in which individuals or entities avoid paying their true tax liability. It has always been a crucial issue for both governments and academic researchers to efficiently detect tax evasion. Recent research has proposed the use of machine learning technology to detect tax evasion and has shown good results in some specific areas. Regrettably, there are still two major obstacles to detect tax evasion. First, it is hard to extract powerful features because of the complexity of tax data. Second, due to the complicated process of tax auditing, labeled data are limited. Such obstacles motivate the contributions of this work. In this paper, we propose a novel tax evasion detection framework named FBNE-PU, a multi-stage method to detect tax evasion in real-life scenarios. In this paper, we perform an in-depth analysis of the characteristics of the transaction network and propose a novel network embedding algorithm, the PnCGCN. It significantly improves detection performance by extracting powerful features from basic features and the tax-related transaction network. Moreover, we utilize nnPU to assign pseudo labels for unlabeled data. Finally, a MLP is trained as the decision function. Experiments on three real-world datasets demonstrate that our method significantly outperforms the comparison methods in the tax evasion detection task.

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.

The identification of tax evasion plays an important role in ensuring tax order, promoting the level of tax collection and management, and reducing tax losses. With the advancements in data mining technology, many machine learning techniques have yielded results in identifying tax evasion. However, to realize satisfactory performance, these models require large amounts of human annotated data. In the tax field, unlabeled tax data are abundant, data annotation in a single region is expensive, and the distributions of characteristics differ among regions; these factors pose substantial difficulties in the development of an identification model. Existing tax evasion detection methods are either trained for single-region tasks, in which case they perform poorly on inter-region tax evasion identification due to the discrepancies in feature distributions, or utilize labeled data from both the target-task field and different but related auxiliary fields to reuse and transfer knowledge of the target domain data, in which case they cannot deal with scenarios in which there are no labeled data in target audit tasks. Although current unsupervised transfer learning techniques can train models in labeled regions for unlabeled regions, large intra-class distribution discrepancies cannot be perfectly minimized in tax evasion detection scenarios. To better address the above challenges, this paper proposes a general architecture, namely, the unsupervised conditional adversarial networks (UCAN) for tax evasion detection, which is the first approach to solve audit tasks in unlabeled target domains via inter-region transfer. Our architecture establishes an adversarial neural network adding label information in the distribution adapter, which can granularly adapt the joint probability distribution (JPD) of the data. We introduce a constraint that is based on the conditional maximum mean discrepancy (CMMD) of the extracted features to align the conditional probability distribution (CPD) of the deep representation. Our model is formed by combining the distribution adapter and the label predictor to realize end-to-end learning of unsupervised feature transfer. The experimental results demonstrate the outstanding performance of our model in all migration tasks compared with state-of-the-art approaches.

Accurate industry classification of national economic activities as an important component in the construction of economic structure and as the basis of the formulation of economic policies and management of national economic activities has been gaining increasing attention. However, owing to the rapid growth in the number of industries, it is become increasingly difficult for tax bureaus to classify the registered taxpayers' industries. Conventional industrial classification methods only focus on the text features, which can not be analyzed and judged comprehensively according to the registration information, and can only carry on single-label classification since they neglect the primary and secondary relationships between the main and subsidiary industries, which can not meet application requirements. To better address these challenges, this paper proposes a model known as attention based bidirectional RNN for hierarchical industry classification (ABR-HIC), which is the first approach, to the best of our knowledge, to simultaneously address comprehensive registration information utilization and multi-label classification for the main and subsidiary industries. Our architecture establishes a bidirectional RNN using a word-attention mechanism, which is able to capture and fully utilize the text and non-text registration information for feature representation. By separating the taxpayer's primary and secondary multi-label classification problem corresponding to the main and subsidiary industries, respectively, into two subtasks and through multi-task learning, our model can provide comprehensive primary and secondary multi-industrial labels. Experiments were conducted on real tax data-sets of the Shaanxi Province, China and the results demonstrate the outstanding performance of our architecture in terms of both the classification effect and training time compared with those of state-of-the-art approaches.

Tax evasion detection plays a crucial role in reducing tax revenue loss and many efforts have been made to develop detection models based on machine learning techniques. To train an effective model to detect tax evaders, a large amount of data is required, especially sufficient labeled data. However, the expensive and time-consuming annotation process results in small amount of labeled data being available, which makes the development of detection models difficult. To address this issue, we propose a tax evasion detection method based on positive and unlabeled learning (TEDM-PU), to identify tax evasion by utilizing limited annotated tax evasion taxpayers and a large amount of unlabeled data. The TEDM-PU framework consists of three stages: a preprocessing stage extracting taxpayer features based on random forest, a pseudo labeling stage assigning pseudo labels to unlabeled samples based on PUAdapter, and a model training stage based on LightGBM method. To evaluate the effectiveness of our proposed TEDM-PU, we conduct experimental tests on real-world tax data. The results demonstrate that TEDM-PU method can detect tax evaders with higher accuracy and better interpretability than state-of-the-art methods.

How do learners schedule their online learning? This issue is concerned by both course instructors and researchers, especially in the context of self-paced online learning environment. Many indicators and methods have been proposed to understand and improve the time management of learning activities, however, there are few tools of visualizing, comparing and exploring the time management to gain intuitive understanding. In this demo, we introduce the LearnExp, an interactive visual analytic system designed to explore the temporal patterns of learning activities and explain the relationships between academic performance and these patterns. This system will help instructors to comparatively explore the distribution of learner activities from multiple aspects, and to visually explain the time management of different learner groups with the prediction of learning performance.

The demo presents TaxVis, a visual detection system for tax auditor. The system supports tax evasion group detection based on a two-phase detection approach. Different from the pattern matching based methods, this two-phase method can analyze the suspicious groups automatically without artificial extraction of tax evasion patterns. In the first phase, we use a network embedding method node2vec to learn representations that embed corporations from a Corporation Associated Network (CANet), and use LightGBM to calculate a suspicious score for each corporation. In the second phase, the system use three detection rules to analyze the transaction anomaly around the suspicious corporations. According to these transaction anomalies, we can discover potential suspicious tax evasion groups. We demonstrate TaxVis on tax data of Shaanxi province in China to verify the usefulness of the system.