Modeling Heterogeneous Graph Network on Fraud Detection
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Bibliographic record
Abstract
Fraud activities in e-commerce, such as spam reviews and fake shopping behaviors, significantly mislead customers' decision making, damage the platforms' reputation, and reduce enterprises' revenue. In recent years, GNN-based models have been widely adopted in fraud detection tasks, which have shown better performance compared to conventional rule-based methods and feature-based models. Most GNN-based models focus on homogeneous graphs, usually including user-to-user, or item-to-item connections. These types of graphs have limitations of eliminating certain types of connections, such as user-item connections. In addition, GNN-based models aggregate neighborhood information based on the assumption that neighbors share the similar structure and content. However, in fraud detection tasks, two major inconsistency issues arise: Severe mixture of structure-inconsistency due to extremely unbalanced positive and negative samples; and mixture of content-inconsistency due to the difference between various item categories. To address the above issues, we propose a Community-based Framework with ATtention mechanism for large-scale Heterogeneous graphs (C-FATH). In order to utilize the entire heterogeneous graph, we directly model on the heterogeneous graph and combine it with homogeneous graphs. The structure-inconsistent nodes are filtered by introducing the community information when constructing neighbors. Content-inconsistent nodes are selected with lower probability by a similarity-based sampling strategy. Further, the model is trained in a multi-task manner that each node type (e.g. user, item, device, order, and review) is associated with a specific loss function. Comprehensive experiments are conducted on two public review datasets and two large-scale datasets from JD.com, and the experimental results demonstrate the effectiveness and scalability of the proposed C-FATH compared to the state-of-the-art approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it