FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework

• URL: http://arxiv.org/abs/2402.12761v1
• Date: Tue, 20 Feb 2024 07:03:59 GMT
• Title: FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
• Authors: Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-kiong Ng
• Abstract summary: Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones. Existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases. We propose an effective federated graph anomaly detection framework (FGAD) to tackle these challenges.
• Score: 33.62637380192881
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases, thereby impeding collaboration among organizations seeking to collectively develop robust GAD models. Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants. To tackle these challenges, we propose an effective federated graph anomaly detection framework (FGAD). We first introduce an anomaly generator to perturb the normal graphs to be anomalous, and train a powerful anomaly detector by distinguishing generated anomalous graphs from normal ones. Then, we leverage a student model to distill knowledge from the trained anomaly detector (teacher model), which aims to maintain the personality of local models and alleviate the adverse impact of non-IID problems. Moreover, we design an effective collaborative learning mechanism that facilitates the personalization preservation of local models and significantly reduces communication costs among clients. Empirical results of the GAD tasks on non-IID graphs compared with state-of-the-art baselines demonstrate the superiority and efficiency of the proposed FGAD method.

Related papers

• Out-of-Distribution Detection on Graphs: A Survey [58.47395497985277]
  Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training. We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches. We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
  arXiv  Detail & Related papers  (2025-02-12T04:07:12Z)
• UMGAD: Unsupervised Multiplex Graph Anomaly Detection [40.17829938834783]
  We propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD. We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs. Then, to weaken the influence of noise and redundant information on abnormal information extraction, we generate attribute-level and subgraph-level augmented-view graphs.
  arXiv  Detail & Related papers  (2024-11-19T15:15:45Z)
• Cluster Aware Graph Anomaly Detection [32.791460110557104]
  We propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label. We show that the proposed similarity-guided loss is a variant of contrastive learning loss.
  arXiv  Detail & Related papers  (2024-09-15T15:41:59Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection [15.757864894708364]
  Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. We propose a novel few-shot Graph Anomaly Detection model called FMGAD. We show that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.
  arXiv  Detail & Related papers  (2023-11-17T07:49:20Z)
• Towards Self-Interpretable Graph-Level Anomaly Detection [73.1152604947837]
  Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. We propose a Self-Interpretable Graph aNomaly dETection model ( SIGNET) that detects anomalous graphs as well as generates informative explanations simultaneously.
  arXiv  Detail & Related papers  (2023-10-25T10:10:07Z)
• GADY: Unsupervised Anomaly Detection on Dynamic Graphs [18.1896489628884]
  We propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets.
  arXiv  Detail & Related papers  (2023-10-25T05:27:45Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach [26.973056364587766]
  Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. We propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short) By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph.
  arXiv  Detail & Related papers  (2022-02-11T09:45:11Z)

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.