Barely Supervised Learning for Graph-Based Fraud Detection
Authors: Hang Yu, Zhengyang Liu, Xiangfeng Luo
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conducted comparative experiments on five real-world datasets using ten currently representative or novel methods to test the performance of our proposed approach. |
| Researcher Affiliation | Academia | Hang Yu*, Zhengyang Liu*, Xiangfeng Luo School of Computer Engineering and Science, Shanghai University, Shanghai, China {yuhang, zhengyangliu, luoxf}@shu.edu.cn |
| Pseudocode | No | The paper describes the model components and equations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that code for other compared methods is published, but does not state that the source code for their own method (BSL) is openly available or provide a link. |
| Open Datasets | Yes | We employ five datasets to evaluate the effectiveness of BSL, with their specific statistical characteristics presented in Table I. Amazon and Yelp (Mc Auley and Leskovec 2013) are datasets of fraudulent comments... Elliptic (Weber et al. 2019) constructs a transaction network of bitcoin... |
| Dataset Splits | Yes | In the experiment, each dataset is divided into three parts a small proportion as labeled samples, another part as unlabeled samples for barely supervised learning, and the remainder used as the test set. For Amazon, Yelp, Elliptic, and T-Finance datasets, the split is 1% : 10%: 89%, while that for T-Social is 0.01%: 0.1%: 99.89% due to its large scale. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | Yes | Our method is implemented in Py Torch 1.13.1 (Paszke et al. 2019) and Py G (Fey and Lenssen 2019) with Python 3.9, and DGL (Wang et al. 2019c) is used for preprocessing the data. |
| Experiment Setup | Yes | The parameters are optimized by Adam (Kingma and Ba 2014), while the learning rate is set as 0.01, and the weight decay rate is 1e-5. We set the embedding size to 96, and the batch size to 256 on five datasets. |