Efficiently modeling user behavior on online payment platforms is crucial for accurately identifying potential financial risks. With the rapid growth of online payment platforms, the volume of user transaction data has significantly increased. Moreover, users' payment behaviors often encompass diverse activities and interactions across multiple scenarios. Based on observations from online payment platforms, we identify three key challenges: scarce labels and poor representation robustness, long user payment behavior sequences, and complex and heterogeneous amount-aware scenarios.

Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed graphs (TTAGs). Such combination of dynamic text semantics and evolving graph structures introduces heightened complexity. Existing TGNNs embed texts statically and rely heavily on encoding mechanisms that biasedly prioritize structural information, overlooking the temporal evolution of text semantics and the essential interplay between semantics and structures for synergistic reinforcement. To tackle these issues, we present $\textbf{CROSS}$, a flexible framework that seamlessly extends existing TGNNs for TTAG modeling. CROSS is designed by decomposing the TTAG modeling process into two phases: (i) temporal semantics extraction; and (ii) semantic-structural information unification. The key idea is to advance the large language models (LLMs) to $\textit{dynamically}$ extract the temporal semantics in text space and then generate $\textit{cohesive}$ representations unifying both semantics and structures. Specifically, we propose a Temporal Semantics Extractor in the CROSS framework, which empowers LLMs to offer the temporal semantic understanding of node's evolving contexts of textual neighborhoods, facilitating semantic dynamics. Subsequently, we introduce the Semantic-structural Co-encoder, which collaborates with the above Extractor for synthesizing illuminating representations by jointly considering both semantic and structural information while encouraging their mutual reinforcement. Extensive experiments show that CROSS achieves state-of-the-art results on four public datasets and one industrial dataset, with 24.7\% absolute MRR gain on average in temporal link prediction and 3.7\% AUC gain in node classification of industrial application.

Urban undisciplined events (UUE) are of increasing concern to urban officials because they reduce the quality of life and cause societal disorder. How to accurately predict future occurrences is a key point in preventing these events. However, existing supervised methods struggle to perform well on sparse UUEs while self-supervised MAE-based methods adopt a traditional random masking strategy which leads to limited performance on UUE forecasting. Fortunately, we have designed an innovative spatiotemporal masking strategy and its corresponding pre-training task called Masked Spatio-Temporal Event Series Modeling (MSTEM). Through Cluster-assisted region masking, MSTEM efficiently distributes masked regions evenly among different clusters, enhancing the model's ability to capture spatial correlation and heterogeneity while addressing sparse region distribution of UUEs. Frequency-enhanced patch masking helps the model to sufficiently extract the temporal features of UUEs by reconstructing multiple views. Additionally, we propose future merge and cluster label modeling to enhance the extraction of spatiotemporal dependencies, thereby improving the performance of MSTEM on downstream prediction tasks. Experimental evaluations on four real-world datasets including crimes and disorderly conduct show that our masked autoencoder with MSTEM outperforms most of the state-of-the-art baselines.

Time series forecasting (TSF) consists of point prediction and probabilistic forecasting. Unlike point forecasting which predicts an expected value of a future target, probabilistic time series forecasting models the uncertainty in data by predicting the distribution of future values, which enhances decision-making flexibility and improves risk management. Traditional probabilistic forecasting methods usually assume a fixed distribution of data, which is not always true for time series. Recently, there have been efforts to adapt diffusion models for time series owing to their exceptional ability to model the distribution of data without prior assumptions. However, how to apply advantages of diffusion models to time series forecasting remains a substantial challenge due to specific issues in time series such as distribution drift and complex dynamic temporal patterns.

Uncivilized urban events disrupt urban order and have a detrimental impact on daily life. Recognizing the significant implications of these events, urban managers strive to proactively prevent them by accurately predicting their future occurrence. However, existing methods overlook crucial contextual information within urban scenarios while mining spatio-temporal dependencies in single event series. Fortunately, we discovered a connection between meteorological conditions and uncivilized events. To leverage this relationship, we propose a novel approach named the Meteorology-Assisted Spatio-Temporal Graph Neural Network (MAST) which integrates meteorological information into the spatio-temporal dependency modeling for predicting urban uncivilized events. Additionally, our approach captures latent regularities in human behavior by explicitly modeling individuals’ psychological states based on meteorological information. We also adopt cross-view contrastive learning between urban regions to dynamically capture the informative components of meteorological information for precise prediction of urban uncivilized events. Experimental evaluations on a real-world dataset demonstrate the superiority of MAST over state-of-theart baselines in terms of predictive performance.