26 citations · 27 across the 4 of their papers we have counts for
5 papers
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…
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…
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,…
End-to-end Learnable Clustering for Intent Learning in Recommendation
Yue Liu, Shihao Zhu, Jun Xia +6
Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer…
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
Yue Liu, Jun Xia, Sihang Zhou +7
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capabil…