34 accepted papers
Detecting spoofing in financial trading is a critical data mining task. Traditional machine learning models often focus on individual node features, failing to capture the contextual relationships among interconnected nodes. Graph-based methodologies have enhanced this by effectively integrating rel
Credit card fraud threatens global payment ecosystems, causing billions in losses and undermining public trust. Efficient fraud detection remains challenging due to surging transaction volumes and evolving tactics. While Graph Neural Networks (GNNs) excel at modeling structural relationships, they s
Illegal related-party transactions (RPT) are federal felonies that pose a severe threat to the stability and integrity of modern financial systems. The increasing frequency of RPTs forms complex and dynamic networks. Existing temporal graph learning methods tend to treat entities as functionally hom
Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). However, real-world time series often show different patterns at different scales, and future changes are shaped by the interplay of these overlapping scales, requiring high-capacity models.…
Large Language Models (LLMs) have made substantial strides in a broad array of natural language tasks. Recently, LLMs have demonstrated potential reasoning capabilities through prompt design, such as the Chain of Thought (CoT). Despite their superiority in question answering, LLMs still face challen…
Graph Neural Networks (GNNs) have achieved significant success in various real-world applications, including social networks, finance systems, and traffic management. Recent researches highlight their vulnerability to backdoor attacks in node classification, where GNNs trained on a poisoned graph mi…
Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to the meticulously designed inter-agent communication topologies. Though impressive in performance, existing multi-agent…
Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynam
Graph Neural Networks (GNNs) demonstrate superior performance in various graph learning tasks, yet their wider real-world application is hindered by the computational overhead when applied to large-scale graphs. To address the issue, the Graph Lottery Hypothesis (GLT) has been proposed, advocating t…
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite the diverse and high-performing designs available…
This paper proposes a novel approach to predicting stock returns based on Relational Temporal Graph Neural Networks (RTGNN). This method provides insights into the dynamic and asymmetric relationships that exist both among different stocks and within individual stocks over time, addressing the limit…
Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often incur significant costs and face challenges in dynamic LLM selection. Current LLM routing methods effectively reduce overhead in single-agent scenarios…
The outbreak of pandemic has a huge impact on production and consumption in the business world, especially for the retail sector. As a crucial component of decision-support technology in the retail industry, sales forecasting is significant for production planning and optimizing the supply of essent
Graph data augmentation (GDA) has shown significant promise in enhancing the performance, generalization, and robustness of graph neural networks (GNNs). However, contemporary methodologies are often limited to static graphs, whose applicability on dynamic graphs—more prevalent in real-world applica…
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD) strategies. While CI overlooks important covariate relationships, CD captures all dependencies without distinction, introducing noise and reducing generalization. Recent advan…
Credit card fraud imposes significant costs on both cardholders and issuing banks. Fraudsters often disguise their crimes, such as using legitimate transactions through several benign users to bypass anti-fraud detection. Existing graph neural network (GNN) models struggle with learning features of…
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset selection have been developed to streamline data volume by \textit{retaining}, \textit{synthesizing}, or \textit{selectin…
Graph Neural Networks (GNNs) have emerged as the leading deep learning models for graph-based representation learning. However, the training and inference of GNNs on large graphs remain resource-intensive, impeding their utility in real-world scenarios and curtailing their applicability in deeper a…
In this paper, we present a novel hierarchical approach to adversarial attacks targeting Graph Neural Networks (GNNs), tailored to overcome the complexities inherent in large-scale poisoning attacks. Traditional global attack strategies often fail to yield effective results on extensive graph struct…
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitation…
In this study, we present $\text{aL\small{LM}4T\small{S}}$, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reconceive time-series forecasting as a self-supervised, multi-patch prediction task, which, compar…
Medical insurance fraud has always been a crucial challenge in the field of healthcare industry. Existing fraud detection models mostly focus on offline learning scenes. However, fraud patterns are constantly evolving, making it difficult for models trained on past data to detect newly emerging frau…
Financial fraud is one of the most significant social issues and has caused tremendous property losses. Graph neural networks (GNNs) have been applied to anti-fraud practices and achieved decent results. However, recent researches have discovered flaws in the robustness of fraud-detection models bas…
Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs. A promising solution is to remove non-essential edges to reduce the computational overheads in GNN. Previous literature generally falls into two categories: topolo…
The networked-loan is major financing support for Micro, Small and Medium-sized Enterprises (MSMEs) in some developing countries. But external shocks may weaken the financial networks' robustness; an accidental default may spread across the network and collapse the whole network. Thus, predicting th…
Medical insurance plays a vital role in modern society, yet organized healthcare fraud causes billions of dollars in annual losses, severely harming the sustainability of the social welfare system. Existing works mostly focus on detecting individual fraud entities or claims, ignoring hidden conspira…
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the performance of various downstream NLP tasks by injecting knowledge facts from large-scale Knowledge Graphs (KGs). However, existing methods for pre-training KEPLMs with relational triples are difficult to be adapted to close domains…
Accurately credit rating on Interbank assets is essential for a healthy financial environment and substantial economic development. But individual participants tend to provide manipulated information in order to attack the rating model to produce a higher score, which may conduct serious adverse eff…
Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to e…
The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures…
Learning from web data has attracted lots of research interest in recent years. However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively. Most existing methods either rely on human supervision or ign…
The guaranteed loan is a debt obligation promise that if one corporation gets trapped in risks, its guarantors will back the loan. When more and more companies involve, they subsequently form complex networks. Detecting and predicting risk guarantee in these networked-loans is important for the loan…
Studies show that motor imagery based Brain-Computer Interface (BCI) systems can be utilized therapeutically in stroke rehabilitation. Efficient decoding of subjects' motor intentions is essential in BCI-based rehabilitation systems to manipulate a neural prosthesis or other devices for motor relear…
Portfolio risk control is vital to financial institutions: investors seek to build equities with the highest return but with minimum risk. However, a general phenomenon is significant comovement among many financial signals, such as stocks and futures. One investment strategy is to choose less corre…