Xiangfeng Luo
Biographic Data
| ID | 10055745 |
|---|---|
| NAME | Xiangfeng Luo |
| GIVEN NAMES | Xiangfeng |
| FAMILY NAME | Luo |
| SIGNATURE | LUO X |
| AFFILIATIONS | Shanghai University of Engineering Science |
| ORCID | 0000-0003-4577-9241 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Robust Graph Fraud Detection Model Based on Adversarial Reweighting
Graph-based fraud detection has gained significant attention due to its effectiveness in risk management. Recent approaches utilizing graph neural networks highlight the benefits of representing nodes through aggregating homogeneous neighbors. However, in real-world situations, fraudsters disguise their activities by forming connections with benign entities, leading to limitations in the homogeneous aggregation method. Because combining represent…
Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis
In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect a…
No prominent works on this page.
Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis
In the field of social computing, the task of aspect-based sentiment analysis (ABSA) aims to classify the sentiment polarity of a given aspect in a sentence. The absence of explicit opinion words in the implicit aspect sentiment expressions poses a greater challenge for capturing their sentiment features in the reviews from social media. Many recent efforts use dependency trees or attention mechanisms to model the association between the aspect a…
A Robust Graph Fraud Detection Model Based on Adversarial Reweighting
Graph-based fraud detection has gained significant attention due to its effectiveness in risk management. Recent approaches utilizing graph neural networks highlight the benefits of representing nodes through aggregating homogeneous neighbors. However, in real-world situations, fraudsters disguise their activities by forming connections with benign entities, leading to limitations in the homogeneous aggregation method. Because combining represent…
Artificial Intelligence (2 works) · Computer Science (2 works) · Advanced Text Analysis Techniques (1 work) · Adversarial system (1 work) · Cognitive science (1 work) · Computer security (1 work) · Data science (1 work) · Graph (1 work) · Graph theory (1 work) · Imbalanced Data Classification Techniques (1 work)