Sheng Xiang

Zihe Wei, Sheng Xiang, Ying Zhang et al.

Graph-structured data underpins applications from citation analysis and social-network modeling to molecular design and knowledge-graph construction, and Large Language Models (LLMs) are increasingly used as prompt-driven graph synthesizers. Classical graph-generation reviews catalog deep generative models and their evaluation primitives, but predate the LLM era and provide no foundation for evaluating instruction-following graph synthesis. Recent LLM-era benchmarks evaluate models along graph-type or task-domain axes; such organizations, however, average over structural complexity and cannot localize where in the complexity spectrum an LLM breaks down. To close this diagnostic gap, we introduce GraphInstruct, a progressive-complexity benchmark that stratifies LLM graph generation into six complexity levels and five evaluation dimensions, paired with 800 hand-authored instructions, 1,582 algorithmically synthesized reference solutions, and a 12-LLM capability evaluation across 45 (model, strategy) configurations. We find that discriminative power peaks at multi-constraint composition rather than reasoning depth, that no single prompting strategy dominates across levels or model families, and that domain-semantic constraints remain iteration-invariant under all tested methods -- pointing to retrieval rather than additional compute as the next research frontier. Atop the benchmark, a verification-guided iterative framework with constraint-aware adaptive prompting consistently surpasses the prompt-engineering ceiling on tested target models, demonstrating that the benchmark's fine-grained signals drive method development.

Dawei Cheng, Yao Zou, Sheng Xiang et al.

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.