GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model

• URL: http://arxiv.org/abs/2503.17903v1
• Date: Sun, 23 Mar 2025 02:40:17 GMT
• Title: GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
• Authors: Yali Fu, Jindong Li, Qi Wang, Qianli Xing,
• Abstract summary: GLADMamba is a novel framework that adapts the selective state space model into UGLAD field.<n>To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD.
• Score: 4.4735289317146405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture the long-range dependencies efficiently and neglect the spectral information. Recently, selective State Space Models (SSMs), particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design View-Fused Mamba (VFM) with a Mamba-Transformer-style architecture to efficiently fuse information from different views with a selective state mechanism. We also design Spectrum-Guided Mamba (SGM) with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refining process. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-F/GLADMamba.

Related papers

• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction.<n>We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation.<n>Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• Neural Architecture Search based Global-local Vision Mamba for Palm-Vein Recognition [42.4241558556591]
  We propose a hybrid network structure named Global-local Vision Mamba (GLVM) to learn the local correlations in images explicitly and global dependencies among tokens for vein feature representation. Thirdly, to learn the complementary features, we propose a ConvMamba block consisting of three branches, named Multi-head Mamba branch (MHMamba), Feature Iteration Unit branch (FIU), and Convolutional Neural Network (CNN) branch. Finally, a Globallocal Alternate Neural Architecture Search (GLNAS) method is proposed to search the optimal architecture of GLVM alternately with the evolutionary algorithm.
  arXiv  Detail & Related papers  (2024-08-11T10:42:22Z)
• MambaLRP: Explaining Selective State Space Sequence Models [18.133138020777295]
  Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest.<n>These models allow efficient processing of long sequences in linear time and are rapidly being adopted in a wide range of applications such as language modeling.<n>To foster their reliable use in real-world scenarios, it is crucial to augment their transparency.
  arXiv  Detail & Related papers  (2024-06-11T12:15:47Z)
• Vision Mamba: A Comprehensive Survey and Taxonomy [11.025533218561284]
  State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. Based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Mamba is expected to become a new AI architecture that may outperform Transformer.
  arXiv  Detail & Related papers  (2024-05-07T15:30:14Z)
• DGMamba: Domain Generalization via Generalized State Space Model [80.82253601531164]
  Domain generalization(DG) aims at solving distribution shift problems in various scenes. Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global receptive fields. We propose a novel framework for DG, named DGMamba, that excels in strong generalizability toward unseen domains.
  arXiv  Detail & Related papers  (2024-04-11T14:35:59Z)
• MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology [10.933433327636918]
  Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology. In this paper, we incorporate the Selective Scan Space State Sequential Model (Mamba) in Multiple Instance Learning (MIL) for long sequence modeling with linear complexity. Our proposed framework performs favorably against state-of-the-art MIL methods.
  arXiv  Detail & Related papers  (2024-03-11T15:17:25Z)
• MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models [56.37780601189795]
  We propose a framework named MamMIL for WSI analysis. We represent each WSI as an undirected graph. To address the problem that Mamba can only process 1D sequences, we propose a topology-aware scanning mechanism.
  arXiv  Detail & Related papers  (2024-03-08T09:02:13Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)

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.