Explainable transaction risk analysis is a challenge for traditional deep learning models, which only predict suspicious transactions without explanations. Current explainable methods rely on hand-crafted rules and lack the ability to automatically generate language-based explanations. Large Language Models (LLMs) offer promise due to their reasoning and text generation abilities but struggle with domain knowledge and hallucinations, making risk analysis difficult. Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. Expert knowledge compensates for domain gaps, while reasoning case retrieval provides step-wise analysis guidance. We incorporate both graph-structured features and semantic features into the retrieval process to enhance the effectiveness of the retrieval. Extensive experiments show that Dual-gRAG improves LLMs' risk analysis capabilities, achieving a 15% increase in different metrics.

Molecular property prediction is crucial for advancing medical research in areas like retrosynthesis analysis and drug discovery. The challenge of obtaining accurate molecular property labels has led to the use of multi-level pretrained Graph Neural Networks (GNNs) with self-supervised learning methods. However, these multi-level approaches do not adequately address relationships across molecular graph levels particularly at the motif and atom levels, and neglect considering the fusion method of different grains. To overcome these limitations, we introduce the Motif-centric Multi-grain Graph Pretaining and Finetuning Strategy Framework (MMGSF). This framework consists of two components: Motif-centric Molecular Graph Pretraining Strategy(MMGS) which focuses on motif-centric contrastive learning on multi-level graph without disturbing molecular structure, and Multi-grain Finetuning (MGF) that refines node representations across grains, using a novel mol-adapter module with cross-attention for adaptive feature fusion. Our MGF captures complex feature interactions, ensuring structural and semantic information from different grains contributes effectively to molecular property predictions. Superior results in molecular property classification tasks demonstrate the effectiveness of MMGSF, and its visualization performance shows that the learned representations capture molecular multi-grain information and properties successfully. This study offers fresh insights into the design of more effective self-supervised learning frameworks for molecular property prediction.

Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance onfixed, hand-crafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing anadaptive neighborhood encoding mechanism. We present SEAN (Selective Encoding for Adaptive Neighborhood), a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node's unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness.