Title: Association-Focused Path Aggregation for Graph Fraud Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: graph fraud detection

graph anomaly detection

graph neural networks

gnn

path-level

path aggregation

long-range dependency

graph data mining

node classification

attention

interpretability

explainability
Abstract: Fraudulent activities have caused substantial negative social impacts and are exhibiting emerging characteristics such as intelligence and industrialization, posing challenges of high-order interactions, intricate dependencies, and the sparse yet concealed nature of fraudulent entities. Existing graph fraud detectors are limited by their narrow "receptive fields", as they focus only on the relations between an entity and its neighbors while neglecting longer-range structural associations hidden between entities. To address this issue, we propose a novel fraud detector based on Graph Path Aggregation (GPA). It operates through variable-length path sampling, semantic-associated path encoding, path interaction and aggregation, and aggregation-enhanced fraud detection. To further facilitate interpretable association analysis, we synthesize G-Internet, the first benchmark dataset in the field of internet fraud detection. Extensive experiments across datasets in multiple fraud scenarios demonstrate that the proposed GPA outperforms mainstream fraud detectors by up to +15% in Average Precision (AP). Additionally, GPA exhibits enhanced robustness to noisy labels and provides excellent interpretability by uncovering implicit fraudulent patterns across broader contexts. Code is available at https://github.com/horrible-dong/GPA.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenges of high-order interactions, intricate dependencies, and the sparsity of fraudulent entities in graph-based fraud detection. The authors point out that existing mainstream fraud detectors suffer from narrow receptive fields, focusing mainly on local neighborhoods and neglecting long-range structural associations. To address these limitations, the paper proposes a novel Graph Path Aggregation (GPA) method that leverages variable-length path sampling, behavior-associated path encoding, path interaction, and aggregation for enhanced fraud detection. Experimental results demonstrate that GPA significantly outperforms mainstream methods, shows superior robustness to noisy labels, and offers stronger interpretability. Additionally, the authors introduce G-Internet, a new synthetic benchmark dataset designed to facilitate research on interpretable fraud detection.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1.  The proposed approach utilizes variable-length path sampling, behavior-associated path encoding, path interaction, and aggregation-enhanced fraud detection, resulting in a much larger receptive field compared to conventional methods.\n2. The method demonstrates stronger robustness to noisy labels, as evidenced by experiments.\n3. The model provides excellent interpretability, allowing for a better understanding of the detected fraudulent patterns.\n4. The introduction of the G-Internet synthetic dataset is a valuable contribution to the community, enabling further research and benchmarking in interpretable fraud detection.\n\nWeakness：\n1. The construction of the synthetic dataset is not sufficiently justified. It remains unclear how well the fixed-rule simulated anomalies reflect real-world fraudulent behaviors, raising concerns about the real-world applicability and generalizability of the results.\n2. The reliance on LLM-generated pseudo-labels for website attributes introduces potential noise and bias. The paper does not assess the reliability of these pseudo-labels or their possible effects on model performance.\n3. The dataset lacks transparency: key details such as user attribute distributions, representative behavioral patterns, and concrete case studies (for both normal and anomalous users) are missing, limiting the ability of the community to fully evaluate the data quality and the scope of the results.\n4.  The evaluation lacks statistical rigor. Given the class imbalance, reporting only mean values (e.g., AP) without standard deviations, confidence intervals, or significance testing makes it difficult to judge the robustness and reproducibility of the reported gains.\n5. The manuscript does not provide an analysis of the boundary conditions for the proposed method.\n\nQUESTIONS:\n1. I am concerned about the dataset construction. While defining anomalous behavior via fixed rules is understandable, it is unclear how the simulated anomalies compare to real-world fraudulent activities. The authors should clarify the representativeness and limitations of the synthetic behaviors.\n2. The paper mentions using an LLM to generate keywords for each website, which essentially acts as pseudo-labeling. The reliability of LLM-generated pseudo-labels should be discussed, and their potential impact on downstream performance should be evaluated.\n3. More details about the dataset are necessary. For example, the distribution of user attributes, typical behavioral examples for individual users, and more case studies of both normal and anomalous instances would greatly help the community assess the dataset quality and method effectiveness.\n4. The reported metrics (especially AP) are sensitive to class imbalance, yet only mean values are presented. The paper lacks standard deviations, confidence intervals, or statistical significance analysis.\n\nLIMITATIONS:\nThe paper does not analyze the boundary conditions under which the proposed method works or fails. I recommend that the authors explicitly discuss the assumptions, potential failure cases, and the method’s robustness to violations of key assumptions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel fraud detection method called Graph Path Aggregation (GPA), which addresses the limitations of existing graph-based fraud detectors by capturing long-range structural associations between entities. The authors propose a framework that includes variable-length path sampling, behavior-associated path encoding, path interaction, and aggregation-enhanced fraud detection. Additionally, they synthesize G-Internet, the first benchmark dataset for internet fraud detection, to facilitate interpretable association analysis. Experiments across multiple fraud scenarios demonstrate that GPA outperforms mainstream methods by up to +15% in Average Precision (AP) and exhibits robustness to noisy labels.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1)\tThe methodology is well-designed, with clear steps from path sampling to fraud detection. The ablation study and robustness tests (e.g., noisy labels) are thorough.\n2)\tThe paper is well-structured, with detailed descriptions of each component (e.g., path encoding, interaction). Figures and tables effectively support the narrative.\n3)\tFraud detection is a critical problem, and the proposed method addresses key challenges (e.g., long-range dependencies, interpretability).\n4)\tThe focus on path-level associations and the integration of behavior features are novel. The approach diverges from traditional GNNs by emphasizing global patterns.\n\nWeaknesses:\n1)\tThe G-Internet dataset is synthetic, which may limit the generalizability of results. Real-world validation is missing.\n2)\tWhile GPA performs well, the computational cost of path sampling/interaction is not deeply analyzed. Scalability for very large graphs (e.g., T-Social) is unclear.\n3)\tThe paper could better differentiate GPA from prior path-based GNNs (e.g., PathNet) and justify the need for variable-length paths.\n\nQUESTIONS:\n1)\tG-Internet is synthetic. How do you plan to validate GPA on real-world data, given privacy constraints? Could you simulate noise/artifacts to better mimic real data?\n2)\tPath sampling/interaction seems expensive, especially for large graphs like T-Social. How does GPA scale compared to baselines (e.g., training time per batch)?\n3)\tThe paper notes that very long paths hurt performance due to \"information redundancy\" What is the optimal range, and how is it determined? Is there a theoretical justification?\n4)\tHow does GPA fundamentally differ from PathNet or RAW-GNN, which also use paths?\n\nLIMITATIONS:\nYes, the authors discuss limitations (e.g., synthetic data, privacy constraints) in Appendix A.9.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a graph-based path sampling and aggregation algorithm to detect fraudulent entities online. The authors argue that path sampling is necessary to capture long-range structural dependencies between entities and unveil fraud patterns. A new dataset is also proposed to simulate the online fraud pattern. Experiments on various datasets demonstrate that the proposed method is better than baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- Path sampling and aggregation is a legitimate solution for graph-based fraud detection, and it is more flexible than GNNs\n- The dataset and baseline selection are comprehensive\n- Interpretability analysis demonstrates the advantage of the proposed GPA method\n\nWeaknesses\n\n- The newly curated dataset is poorly described. GPA's AUC already hitting 99.8% leaves no improvement space for follow-up work\n- The definitions and equations in Sec. 3 are introduced based on the proposed Internet Fraud dataset, while GPA has been tested on five other datasets as well. It's unclear how GPA generalizes to those graph datasets that do not have the same node/edge types and features\n\nQUESTIONS:\n- What is the longest path that the GPA is encoded in the G-Internet dataset?\n- Does GPA distinguish between behavior feature and node feature in datasets other than G-Internet?\n- According to Table 4, the behavior feature plays a very critical role in boosting fraud detection performance. I am wondering if other baselines also use the behavior feature as part of the node feature?\n\nLIMITATIONS:\n- The Sec. 3 should be generalizable to all graph fraud datasets instead of G-Internet only\n- The dataset introduction in Sec. 4 is confusing; I need to read the Appendix to fully understand the dataset construction process\n- I appreciate the author's effort in building a new fraud dataset simulating real-world fraud patterns, but the near-perfect performance makes the dataset almost useless", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel method called Graph Path Aggregation (GPA) to overcome the “receptive field” limitations commonly observed in existing GNN-based fraud detection models. GPA samples variable-length paths from each user node, encodes these paths by incorporating behavioral features, and leverages self-attention mechanisms to model interactions among paths, thereby capturing long-range structural associations. The aggregated path-level information is then used to predict whether a user is fraudulent or benign. Additionally, the authors introduce G-Internet, a new benchmark dataset specifically designed for internet fraud detection. Through extensive experiments across multiple domains—including finance, online reviews, and social networks—the proposed method demonstrates up to 15% improvement in Average Precision (AP) compared to existing approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\t•\tNovel path-based approach: Unlike conventional GNNs that primarily rely on 1-hop neighbor information, the proposed GPA method captures long-range structural dependencies by aggregating path-level information combined with behavioral features. This represents a significant modeling innovation.\n\t•\tStrong performance and robustness: GPA consistently outperforms existing methods across multiple domains. Notably, it maintains a high AP of 83.8% even under 80% label noise, demonstrating strong robustness to noisy annotations.\n\t•\tInterpretability contribution: The analysis of attention maps (Figures 2 and 3) reveals that the model assigns higher attention weights to fraud-related paths, offering a visually intuitive explanation that aligns well with domain knowledge and enhances interpretability.\n\t•\tContribution of the G-Internet dataset: Existing fraud detection datasets are often anonymized, making interpretability difficult. G-Internet, constructed based on well-defined fraud rules, provides a transparent, interpretable benchmark that facilitates the evaluation of association-focused detection methods.\n\nWeaknesses:\n\t•\tLimitations of G-Internet as a simulation-based dataset: Since G-Internet is synthetically generated based on 12 predefined rules rather than real-world data, there is a risk that the model may overfit to these handcrafted patterns. As such, it may fail to generalize to the complexity and evolving nature of actual fraud behavior.\n\t•\tLimited path lengths in certain datasets: Although the paper emphasizes capturing long-range dependencies, the maximum path length is only 5 in some datasets (e.g., Elliptic, T-Finance). This narrow scope may weaken the claim of capturing truly long-range associations and reduce the distinctiveness compared to traditional GNNs.\n\t•\tLack of computational complexity and scalability analysis: The GPA framework involves intensive operations such as path sampling, behavior feature construction, and attention-based interaction. However, the paper does not provide an analysis of computational complexity (e.g., O-notation) or discuss the scalability of the approach to large-scale graphs.\n\t•\tDisparity in baseline performance: In Table 2, many baseline models perform extremely poorly on G-Internet, while GPA achieves substantially higher scores. This raises concerns that the dataset might be tailored to favor the proposed method.\n\t•\tLimited evaluation metrics: In fraud detection, Recall is often more critical than Precision. However, the paper evaluates performance using only AUC and Average Precision (AP). Including metrics like Recall or F1-score would provide a more comprehensive and practically relevant evaluation.\n\nQUESTIONS:\n1.\tThe G-Internet dataset is simulation-based and may not fully capture the dynamic and evolving patterns of real-world fraud. Do the authors have any plans to validate the model on real datasets or to collaborate with external partners for real-world evaluation?\n\t2.\tFor datasets where the maximum path length is limited to 5 (e.g., Elliptic or T-Finance), it is unclear whether GPA captures fundamentally different information compared to standard GNNs like GCN or GraphSAGE. Do the authors have plans to provide more experimental evidence to support the distinctiveness of GPA under such settings?\n\t3.\tGiven the computational complexity of GPA—including path sampling, behavior encoding, and attention-based interactions—it would be helpful to include an analysis of time and space complexity. Can the authors provide further discussion on the scalability of GPA to large-scale graphs (e.g., with tens of millions of nodes)?\n\t4.\tIn fraud detection tasks, Recall is often more critical than Precision. Beyond Average Precision (AP), could the authors also report metrics such as Recall@K or F1-score to more comprehensively demonstrate the effectiveness of GPA?\n\nLIMITATIONS:\nThe authors have not adequately addressed the limitations or potential negative societal impacts of their work. The paper lacks discussion on key limitations such as the limited realism of the simulation-based G-Internet dataset, risks of overfitting to predefined fraud patterns, computational scalability, and the narrow scope of evaluation metrics. Moreover, it does not consider potential societal consequences, including deployment risks in high-stakes settings, the possibility of reinforcing biases through behavior-based features, and the dangers of premature adoption due to overclaimed performance.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}