26 citations · 27 across the 4 of their papers we have counts for
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cs.AI2026
LLM-Enhanced Heterogeneous Graph Learning with Business Semantics for Financial Fraud Detection
Yixuan Chen, Hongyu Zhan, Jie Sheng +5
Financial fraud detection increasingly relies on heterogeneous transaction graphs, in which entities such as cards and merchants play distinct business roles and the risk meaning o…
cs.LG2026
FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection
Yixuan Chen, Hongyu Zhan, Jie Sheng +5
The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconn…
cs.LG2026
LoReC: Rethinking Large Language Models for Graph Data Analysis
Hongyu Zhan, Qixin Wang, Yusen Tan +6
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies,…