Collaboration Based Multi-Label Propagation for Fraud Detection
Authors: Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that the proposed method not only outperforms on ordinary multi-label datasets, but is effective and scalable on large-scale e-commerce dataset. Section 4: Experiments. Table 1: Transductive performance comparison on ordinary multi-label datasets. Table 2: Transductive performance comparison of three graph-based algorithms on Taobao-FUD dataset. |
| Researcher Affiliation | Collaboration | 1Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University 2Alibaba Group, Hangzhou, China 3School of Computer Science, Wuhan University |
| Pseudocode | No | The paper describes the algorithms using mathematical equations and descriptive text but does not include a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | We choose four real-world multi-label datasets from different task domains: 1) Medical [Pestian et al., 2007]: a text dataset... 2) Image [Wang et al., 2019]: a collection of... 3) Slashdot [Read et al., 2009]: a web text dataset... 4) Eurlex-sm [Loza Menc ia and F urnkranz, 2008]: a large text dataset... |
| Dataset Splits | Yes | All the datasets are randomly partitioned to 5% labeled data and 95% unlabeled data. We randomly select 5% examples as labeled data and the rest are used to evaluate the transductive performance. |
| Hardware Specification | No | The computations are performed on Max Compute platform, a fast, distributed and fully hosted GB/TB/PB level data warehouse solution. We use three computation instances for time comparison and 3000 instances for performance comparison. This mentions a platform and generic instances but no specific hardware components like CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions applying a three-layer neural network and building a k-NN adjacency graph, but it does not specify any software names with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | For our methods, γ is selected from {0.1, 1, 10, 100}. α is chosen from {0.01, 0.05, 0.1, 0.2, 0.5}. β, λ and µ are empirically fixed to 0.1. For Deep Fraud, we apply a three layer neural network with Re LU activation. The hidden size is set as 128. The learning rate and regularization parameter are set as 0.001 and 0.5. When building graphs, k is set as 20. |