A Pre-Training and Adaptive Fine-Tuning Framework for Graph Anomaly Detection

• URL: http://arxiv.org/abs/2504.14250v1
• Date: Sat, 19 Apr 2025 09:57:35 GMT
• Title: A Pre-Training and Adaptive Fine-Tuning Framework for Graph Anomaly Detection
• Authors: Yunhui Liu, Jiashun Cheng, Jia Li, Fugee Tsung, Hongzhi Yin, Tieke He,
• Abstract summary: Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet it remains challenging due to the scarcity of abnormal nodes and the high cost of label annotations.<n>We propose PAF, a framework specifically designed for GAD that combines low- and high-pass filters in the pre-training phase to capture the full spectrum of frequency information in node features.
• Score: 67.77204352386897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet it remains challenging due to the scarcity of abnormal nodes and the high cost of label annotations. Graph pre-training, the two-stage learning paradigm, has emerged as an effective approach for label-efficient learning, largely benefiting from expressive neighborhood aggregation under the assumption of strong homophily. However, in GAD, anomalies typically exhibit high local heterophily, while normal nodes retain strong homophily, resulting in a complex homophily-heterophily mixture. To understand the impact of this mixed pattern on graph pre-training, we analyze it through the lens of spectral filtering and reveal that relying solely on a global low-pass filter is insufficient for GAD. We further provide a theoretical justification for the necessity of selectively applying appropriate filters to individual nodes. Building upon this insight, we propose PAF, a Pre-Training and Adaptive Fine-tuning framework specifically designed for GAD. In particular, we introduce joint training with low- and high-pass filters in the pre-training phase to capture the full spectrum of frequency information in node features. During fine-tuning, we devise a gated fusion network that adaptively combines node representations generated by both filters. Extensive experiments across ten benchmark datasets consistently demonstrate the effectiveness of PAF.

Related papers

• Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs [60.82508765185161]
  We propose Dual-Frequency Filtering Self-aware Graph Neural Networks (DFGNN) DFGNN integrates low-pass and high-pass filters to extract smooth and detailed topological features. It dynamically adjusts filtering ratios to accommodate both homophilic and heterophilic graphs.
  arXiv  Detail & Related papers  (2024-11-18T04:57:05Z)
• Partitioning Message Passing for Graph Fraud Detection [57.928658584067556]
  Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks.<n>Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily.<n>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.
  arXiv  Detail & Related papers  (2024-11-16T11:30:53Z)
• Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach [58.8524608686851]
  Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for homophilic graphs and a high-pass filter for heterophilic graphs. We introduce a novel GNN framework Node-MoE that utilizes a mixture of experts to adaptively select the appropriate filters for different nodes.
  arXiv  Detail & Related papers  (2024-06-05T17:12:38Z)
• Alleviating Structural Distribution Shift in Graph Anomaly Detection [70.1022676681496]
  Graph anomaly detection (GAD) is a challenging binary classification problem. Gallon neural networks (GNNs) benefit the classification of normals from aggregating homophilous neighbors. We propose a framework to mitigate the effect of heterophilous neighbors and make them invariant.
  arXiv  Detail & Related papers  (2024-01-25T13:07:34Z)
• 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)
• Heterophily-Aware Graph Attention Network [42.640057865981156]
  Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. We propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily.
  arXiv  Detail & Related papers  (2023-02-07T03:21:55Z)
• Node-oriented Spectral Filtering for Graph Neural Networks [38.0315325181726]
  Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data. In general, learning a universal spectral filter on the graph from the global perspective may still suffer from great difficulty in adapting to the variation of local patterns. We propose the node-oriented spectral filtering for graph neural network (namely NFGNN)
  arXiv  Detail & Related papers  (2022-12-07T14:15:28Z)
• Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs [6.018995094882323]
  Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. Most GNNs assume local homophily, i.e., strong similarities in localneighborhoods. We propose a flexible GNN model, which is capable of handling any graphs without beingrestricted by their underlying homophily.
  arXiv  Detail & Related papers  (2021-03-26T00:35:36Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)

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.