Partitioning Message Passing for Graph Fraud Detection

• URL: http://arxiv.org/abs/2412.00020v1
• Date: Sat, 16 Nov 2024 11:30:53 GMT
• Title: Partitioning Message Passing for Graph Fraud Detection
• Authors: Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen,
• Abstract summary: Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily. In our work, we argue that the key to applying GNNs for GFD is not to exclude but to em distinguish neighbors with different labels.
• Score: 57.928658584067556
• License:
• Abstract: Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily, by excluding heterophilic neighbors during message passing. In our work, we argue that the key to applying GNNs for GFD is not to exclude but to {\em distinguish} neighbors with different labels. Grounded in this perspective, we introduce Partitioning Message Passing (PMP), an intuitive yet effective message passing paradigm expressly crafted for GFD. Specifically, in the neighbor aggregation stage of PMP, neighbors with different classes are aggregated with distinct node-specific aggregation functions. By this means, the center node can adaptively adjust the information aggregated from its heterophilic and homophilic neighbors, thus avoiding the model gradient being dominated by benign nodes which occupy the majority of the population. We theoretically establish a connection between the spatial formulation of PMP and spectral analysis to characterize that PMP operates an adaptive node-specific spectral graph filter, which demonstrates the capability of PMP to handle heterophily-homophily mixed graphs. Extensive experimental results show that PMP can significantly boost the performance on GFD tasks.

Related papers

• SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network [38.669815079957566]
  Graph Neural Network (GNN) with the main idea of encoding graph structure information of graphs by propagation and aggregation has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as homogeneous graphs, heterogeneous graphs, and more complex graphs like knowledge graphs. For the phenomenon of performance degradation in deep GNNs, we propose a new perspective.
  arXiv  Detail & Related papers  (2024-07-03T02:40:39Z)
• Spatio-Spectral Graph Neural Networks [50.277959544420455]
  We propose Spatio-Spectral Graph Networks (S$2$GNNs) S$2$GNNs combine spatially and spectrally parametrized graph filters. We show that S$2$GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs.
  arXiv  Detail & Related papers  (2024-05-29T14:28:08Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum [26.62679288320554]
  Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. This paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors.
  arXiv  Detail & Related papers  (2023-12-11T15:18:51Z)
• GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily [15.93465948768545]
  We demystify the impact of graph heterophily on graph neural networks (GNNs) filters. We propose a simple yet powerful GNN named GPatcher by leveraging the patch-Mixer architectures. Our model demonstrates outstanding performance on node classification compared with popular homophily GNNs and state-of-the-art heterophily GNNs.
  arXiv  Detail & Related papers  (2023-06-25T20:57:35Z)
• RU-Net: Regularized Unrolling Network for Scene Graph Generation [92.95032610978511]
  Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1) ambiguous object representations, and 2) low diversity in relationship predictions. We propose a regularized unrolling network (RU-Net) to address both problems.
  arXiv  Detail & Related papers  (2022-05-03T04:21:15Z)
• Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily [58.76759997223951]
  We propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs. We also propose a Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets.
  arXiv  Detail & Related papers  (2022-03-19T14:26:43Z)
• Boosting Graph Neural Networks by Injecting Pooling in Message Passing [4.952681349410351]
  We propose a new, adaptable, and powerful MP framework to prevent over-smoothing. Our bilateral-MP estimates a pairwise modular gradient by utilizing the class information of nodes. Experiments on five medium-size benchmark datasets indicate that the bilateral-MP improves performance by alleviating over-smoothing.
  arXiv  Detail & Related papers  (2022-02-08T08:21:20Z)
• On Local Aggregation in Heterophilic Graphs [11.100606980915144]
  We show that properly tuned classical GNNs and multi-layer perceptrons match or exceed the accuracy of recent long-range aggregation methods on heterophilic graphs. We propose the Neighborhood Information Content(NIC) metric, which is a novel information-theoretic graph metric.
  arXiv  Detail & Related papers  (2021-06-06T19:12:31Z)
• Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing [88.30867628592112]
  Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. We show that existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. In order to preserve node proximities, we augment the existing GNNs with node representations.
  arXiv  Detail & Related papers  (2020-09-05T16:46:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.