Real bank-transaction data are rarely available to non-regulatory parties due to privacy and compliance constraints, while public substitutes often mismatch financial semantics, limiting the transferability and reproducibility of anomaly-detection research in financial settings. To address this, we release F2-Gen: an open-source, scenario-driven, configurable web platform that generates synthetic data covering both tabular transaction records and transaction graphs for modeling and evaluating financial-fraud behaviors. F2-Gen provides parameterized fraud scenarios, interactive visual analysis, automated screening to remove label-revealing fields, tutorials, and batch data export. Use cases include anomaly detection, graph learning, and teaching demonstrations; standard benchmarking is supported via a screened feature subset, ready-to-run baseline scripts, and sanity/controllability checks. Project page: https://sethgu.github.io/FinancialFraudDataGenerator/. Code: https://github.com/sethGu/FinancialFraudDataGenerator.

Unsupervised graph anomaly detection seeks to discover rare patterns in graph-structured data, enabling applications across diverse fields such as financial transactions, e-commerce, and beyond. Although recent methods based on graph autoencoder and graph contrastive consistency show considerable promise in this field, the former primarily addresses global anomalies, whereas the latter is more focused on local anomalies. This distinction limits their capacity to fully capture the abnormalities of nodes in the graph. Moreover, challenges such as overfitting of abnormal patterns and the introduction of unknown noise from random walk-based subgraph sampling remain prevalent. These issues contribute to suboptimal model performance. To tackle these challenges, we propose Local and Global Contrastive Networks (LGCN). LGCN first extracts graph features using a feature encoder module and subsequently projects them into local and global feature subspaces. The global information learning module mitigates overfitting in reconstruction techniques to abnormal patterns by simultaneously accounting for both the similarities and differences between nodes. The local information learning module extracts anomaly information by evaluating the consistency between the center node and its corresponding context features. Extensive experiments across eight datasets demonstrate that LGCN outperforms existing methods in unsupervised graph anomaly detection.

Knowledge Base Question Answering (KBQA) aims to answer natural language questions by reasoning over large-scale structured Knowledge Bases (KBs). Among existing approaches, semantic parsing-based methods have emerged as a mainstream solution, where Large Language Models (LLMs) are employed to translate questions into structured graph queries such as Logical Forms (LFs). However, this paradigm faces two critical challenges: (1) The complex semantic mapping and graph retrieval operations render direct one-shot LF generation difficult; (2) LLMs suffer from inherent hallucination issues, generating semantically plausible-seeming but factually incorrect or invalid LFs, which are non-executable. To address these challenges, this article proposes HP-Corr , a novel framework that integrates H op-wise P lanning with iterative explainable self- Corr ection for faithful knowledge reasoning. Specifically, the system utilizes a fine-tuned open source LLM for query planning and explainable self-correction. The query planner generates reasoning paths hop-by-hop, while an explainable self-correction provides hop-wise feedback, enabling interpretable path editing based on existing reasoning paths and retrieved KB knowledge. By introducing the dual-module cooperative architecture, our system performs iterative plan-then-correct to refine query paths progressively, ensuring answer reliability and LFs executability. Experimental results demonstrate significant improvements, with our approach achieving higher accuracy while substantially reducing the search space, particularly in complex multi-hop KBQA scenarios.

Graph anomaly detection plays a crucial role in identifying nodes that deviate significantly from normal patterns within a graph, with applications spanning various domains such as detection of authorship fraud and rumor propagation. Traditional methods primarily focus on aggregating information from neighboring nodes and reconstructing the central node based on these aggregated features. The anomaly degree is then calculated by comparing the reconstructed features with the original ones. Despite their effectiveness, these methods face limitations due to the constraints of device performance and the need to protect user privacy. In reality, graph data is often partitioned and distributed across different local clients, which leads to isolated client subgraphs. This partitioning results in incomplete feature aggregation, as the connections between subgraphs are missing, ultimately reducing the performance of anomaly detection models. To overcome these challenges, a federated graph anomaly detection approach based on disentangled representation learning is proposed. This method separates node features into two distinct components: intrinsic features and subgraph style features. By identifying outliers within the subgraph style features, a set of pseudo-nodes is generated and shared across the entire graph. These pseudo-nodes simulate connections between otherwise isolated subgraphs, which enables more comprehensive aggregation of intrinsic features from neighboring nodes. In addition, conditional variational autoencoders (CVAE) are employed alongside contrastive learning strategies to alleviate class imbalance and achieve effective feature disentanglement. These techniques help ensure that anomalous nodes are detected more accurately despite the inherent challenges of federated graph systems. Extensive experiments conducted on six diverse datasets provide compelling evidence of the proposed method's superior performance in federated graph anomaly detection, highlighting its ability to effectively handle incomplete graph structures while maintaining data privacy.

Recent trends in next-item recommendation systems have focused on modeling user intents. Traditional methods often extract users' inherent intents from the most representative items in a session, overlooking “unexpected items” that deviate from the majority in various contextual aspects. These unexpected items, frequently present, can be crucial indicators of a user's inclination towards exploring new options, signaling emerging intents that warrant significant attention. In response, we introduce DbMei, a novel approach that decouples user behaviors and emphasizes the modeling of emerging intents. DbMei distinguishes between two user behavior types: “focused shopping”, which aligns with users' inherent intents, and”wandering shopping”, which aligns with emerging intents. Focused shopping is analyzed using topic modeling and hypergraph learning while wandering shopping is explored through session neighbor retrieval. An exploitation-exploration mechanism is employed to determine the behavioral probability distribution for upcoming items. This integrated modeling of focused and wandering shopping behaviors drives our recommendation process. Extensive empirical studies on two real-world datasets, Amazon-KDD and Beauty, showcase DbMei's superiority over leading methods regarding Recall and MRR metrics. Our code is publicly available at https://github.com/sunlingdan-123/DbMei.

Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.

The understanding of people’s inter-regional mobility behaviors, such as predicting the next activity region (AR) or uncovering the intentions for regional mobility, is of great value to public administration or business interests. While there are numerous studies on human mobility, these studies are mainly from a statistical view or study movement behaviors within a region. The work on individual-level inter-regional mobility behavior is limited. To this end, in this article, we propose a dynamic region-relation-aware graph neural network (DRRGNN) for exploring individual mobility behaviors over ARs. Specifically, we aim at developing models that can answer three questions: (1) Which regions are the ARs? (2) Which region will be the next AR, and (3) Why do people make this regional mobility? To achieve these tasks, we first propose a method to find out people’s ARs. Then, the designed model integrates a dynamic graph convolution network (DGCN) and a recurrent neural network (RNN) to depict the evolution of relations between ARs and mine the regional mobility patterns. In the learning process, the model further considers peoples’ profiles and visited point-of-interest (POIs). Finally, extensive experiments on two real-world datasets show that the proposed model can significantly improve accuracy for both the next AR prediction and mobility intention prediction.

Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

The evolution potential estimation of news events can support the decision making of both corporations and governments. For example, a corporation could manage its public relations crisis in a timely manner if a negative news event about this corporation is known with large evolution potential in advance. However, existing state-of-the-art methods are mainly based on time series historical data, which are not suitable for the news events with limited historical data and bursty properties. In this article, we propose a purely content-based method to estimate the evolution potential of the news events. The proposed method considers a news event at a given time point as a system composed of different keywords, and the uncertainty of this system is defined and measured as the Semantic Uncertainty of this news event. At the same time, an uncertainty space is constructed with two extreme states: the most uncertain state and the most certain state. We believe that the Semantic Uncertainty has correlation with the content evolution of the news events, so it can be used to estimate the evolution potential of the news events. In order to verify the proposed method, we present detailed experimental setups and results measuring the correlation of the Semantic Uncertainty with the Content Change of news events using collected news events data. The results show that the correlation does exist and is stronger than the correlation of value from the time-series-based method with the Content Change. Therefore, we can use the Semantic Uncertainty to estimate the evolution potential of news events.

Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in orderto capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm witheach conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.

No abstract available.

With the rapid development of the Web, information on the Web, especially for textual information, has come to an extremely large amount and increases very fast every day. It has become much more difficult for people to find their interested or demanded information from the Web. Thus how to automatically and intelligently acquire the textual semantics from the Web has become an increasingly important issue today. In this paper, based on human memory and reading process, we propose a machine-oriented text understanding framework, which can guide machines to read texts with human ways so as to effectively acquire the semantics in texts and then further help people to find out the proper information they want. The proposed framework is on the basis of different kinds of memory systems in cognitive psychology and accordingly consists of four main parts, including shallow semantics acquiring model (SSAM), shallow-deep semantics processing model (SDSPM), episodic semantics activating model (ESAM) and background knowledge activating model (BKAM). A case study has been carried out to further specify the proposed framework.