SparseFraudNet: A Graph-based Approach for Cold-start Fraud Detection with Information Aggregation
Bibliographic record
Abstract
Online reviews play a critical role in influencing consumer’s purchasing decision on e-commerce, making them a prime target for manipulation through fraudulent reviews. Although various Fraud Detection (FD) techniques have been presented, a crucial problem still remains unaddressed, i.e., the cold-start problem in FD, which refers to the difficulty in identifying fraudulent reviews due to limited historical data for new users and new products. Existing graph-based detection methods, while effective for well-connected nodes, are suffering Sparse Graph (SG) connections in cold-start FD. In this paper, we propose a novel approach called SparseFraudNet to address the problem of cold-start FD with information aggregation. Specifically, the local information aggregation is proposed to dynamically optimize neighbor selection using Reinforcement Learning (RL) with Bernoulli Multi-Armed Bandit (BMAB), with the goal to capture the five key types of relations among reviews. The global information aggregation is proposed to leverage Graph Coarsening (GC) with manifold learning and spectral clustering to mitigate adjacency matrix sparsity for new users under new products using Sparse Spectral Clustering (SSC). Experiments on the YelpZip-Cold and YelpNYC-Cold datasets demonstrate that the proposed SparseFraudNet approach significantly outperforms state-of-the-art methods in FD in terms of accuracy, precision, recall, F1 measure and AUC to identify fraudulent reviews of new users under new products.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".