Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed real-life temporal graphs. In this paper, we first give an overview of the existing temporal graph generators, including recently emerged learning-based approaches. Most of these learning-based methods suffer from one of the limitations: low efficiency in training or slow generating, especially for temporal random walk-based methods. Therefore, we propose an efficient learning-based approach to generate graph snapshots, namely temporal graph autoencoder (TGAE). Specifically, we propose an attention-based graph encoder to encode temporal and structural characteristics on sampled ego-graphs. And we proposed an ego-graph decoder that can achieve a good trade-off between simulation quality and efficiency in temporal graph generation. Finally, the experimental evaluation is conducted among our proposed TGAE and representative temporal graph generators on real-life temporal graphs and synthesized graphs. It is reported that our proposed approach outperforms the state-of-the-art temporal graph generators by means of simulation quality and efficiency.

Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overlooking the broader context of interconnected nodes. Graph-based techniques, particularly Graph Neural Networks (GNNs), have advanced the field by leveraging relational information effectively. However, in real-world spoofing detection datasets, trading behaviors exhibit dynamic, irregular patterns. Existing spoofing detection methods, though effective in some scenarios, struggle to capture the complexity of dynamic and diverse, evolving inter-node relationships. To address these challenges, we propose a novel framework called the Generative Dynamic Graph Model (GDGM), which models dynamic trading behaviors and the relationships among nodes to learn representations for conspiracy spoofing detection. Specifically, our approach incorporates the generative dynamic latent space to capture the temporal patterns and evolving market conditions. Raw trading data is first converted into time-stamped sequences. Then we model trading behaviors using the neural ordinary differential equations and gated recurrent units, to generate the representation incorporating temporal dynamics of spoofing patterns. Furthermore, pseudo-label generation and heterogeneous aggregation techniques are employed to gather relevant information and enhance the detection performance for conspiratorial spoofing behaviors. Experiments conducted on spoofing detection datasets demonstrate that our approach outperforms state-of-the-art models in detection accuracy. Additionally, our spoofing detection system has been successfully deployed in one of the largest global trading markets, further validating the practical applicability and performance of the proposed method.

Credit card fraud is a severe issue that causes significant losses for both cardholders and issuing banks. Existing methods utilize machine learning-based classifiers to identify fraudulent transactions from labeled transaction records. However, labeled data are often scarce compared to the billions of real transactions due to the high cost of annotation, which means that previous methods do not fully utilize the rich features of unlabeled data. What’s more, contemporary methods succumb to a fallacy of unawareness of the local risk structure and the inability to capture certain risk patterns. Therefore, we propose the Risk-aware Gated Temporal Attention Network (RGTAN) for fraud detection in this work. Specifically, we first build a temporal transaction graph based on the transaction records, which consists of temporal transactions (nodes) and their interactions (edges). Then we leverage a Gated Temporal Graph Attention (GTGA) Mechanism to propagate messages among the nodes and learn adaptive representations of transactions. We also model the fraud patterns through risk propagation, taking advantage of the relations among transactions. More importantly, we devise a neighbor risk-aware representation learning layer to enhance our method’s perception of multi-hop risk structures. We conduct extensive experiments on a real-world credit card transaction dataset and two public fraud detection datasets. The results show that our proposed method, RGTAN, outperforms other state-of-the-art methods on three fraud detection datasets. The risk-aware semi-supervised experiments also demonstrate the excellent performance of our model with only a small fraction of manually labeled data. Moreover, RGTAN has been deployed in a world-leading credit card issuer for credit card fraud detection, and the case study results show the effectiveness of our method in uncovering real-world fraud patterns.

Uncertain graphs have been widely used to model complex linked data in many applications, such as guaranteed-loan networks and power grids. In these networks, a node usually has a certain chance of default due to self-factors or the influence from upstream nodes. For regulatory authorities, it is critical to efficiently identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the top-k vulnerable nodes detection problem in uncertain graphs. We formally define the model and prove it hardness. A sampling-based approach is first proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results. Novel optimization techniques and a bottom-k sketch based approach are further developed to scale for large networks. We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. Moreover, to further verify the advantages of our model, we integrate the proposed techniques with our loan risk control system, which is deployed in the collaborated bank. Particularly, we show that our proposed model can better estimate the default risks of enterprises compared to the state-of-the-art techniques.

Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.

The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines.

Uncertain graphs have been widely used to model complex linked data in many applications, such as guaranteed-loan networks and power grids. In these networks, a node usually has a certain chance of default due to self-factors or the influence from upstream nodes. For regulatory authorities, it is critical to efficiently and accurately identify the vulnerable nodes, i.e., nodes with high default risk, such that people could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the top-k vulnerable nodes detection problem in uncertain graphs. Due to the hardness of the problem, sampling-based methods are proposed with tight theoretical guarantee. We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks.

Graph generation is beneficial to comprehend the creation of meaningful structures of networks in a broad spec-trum of applications such as social networks and biological net-works. Recent studies tend to leverage deep learning techniques to learn the topology structures in graphs. However, we notice that the community structure, which is one of the most unique and prominent features of the graph, cannot be well captured by the existing graph generators. Moreover, the existing advanced deep learning-based graph generators are not efficient and scalable, which can only handle small graphs. In this paper, we propose a novel community-preserving generative adversarial network (CPGAN) for effective and efficient (scalable) graph simulation. We employ graph convolution networks in the encoder and share parameters in the generation process to transmit information about community structures and preserve the permutation-invariance in CPGAN. We conducted extensive experiments on benchmark datasets, including six sets of real-life graphs. The results demonstrate that CPGAN can achieve a good trade-off between efficiency (scalability) and graph simulation quality for real-life graph simulation compared with state-of-the-art baselines.