Spectral Analysis of Fake News Propagation

• URL: http://arxiv.org/abs/2605.13861v1
• Date: Sat, 18 Apr 2026 14:55:54 GMT
• Title: Spectral Analysis of Fake News Propagation
• Abstract summary: We study news propagation from a spectral view by connecting graph spectra to propagation-related structural properties through rigorous spectral bounds.<n>In particular, we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation.<n>We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns.
• Score: 3.685453775072903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The propagation structure of fake news has been shown to be an important cue for detecting it; yet, existing propagation-based fake news detection methods have mainly relied on ad hoc topological features, and a unified view of cascade patterns is still lacking. To address this, we study news propagation from a spectral view by connecting graph spectra to propagation-related structural properties through rigorous spectral bounds. In particular, we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns. For efficient optimization, we rely on a first-order perturbation approximation and consider both score-guided and bound-guided objectives. Experiments on real-world data reveal meaningful spectral differences between fake and real news, competitive classification performance from spectral bounds, and interpretable evolution trajectories from structural optimization. The findings demonstrate the value of spectral analysis for understanding and modeling news propagation.

Related papers

• Revisiting Spectral Representations in Generative Diffusion Models [98.43552218868837]
  Diffusion models have shown remarkable performance on diverse generation tasks.<n>Recent work finds that imposing representation alignment on the hidden states of diffusion networks can both facilitate training convergence and enhance sampling quality.<n>We investigate the connection between self-supervised spectral representation learning and diffusion generative models through a shared perspective on perturbation kernels.
  arXiv  Detail & Related papers  (2026-09-08T04:54:02Z)
• Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection [29.979358462284623]
  We propose a lightweight and efficient Spectral-Spatial Synergistic Guided Network (S3GNet) for object detection.<n>S3GNet builds a closed-loop information flow around spectrum robust modeling, cross-stream co-perception and multi-scale refinement decoding.<n> Experimental results show that S3GNet achieves superior performance in both computational efficiency and detection accuracy compared to existing methods.
  arXiv  Detail & Related papers  (2026-07-23T08:14:32Z)
• Spectral Graph Reasoning Network for Hyperspectral Image Classification [0.43512163406551996]
  Convolutional neural networks (CNNs) have achieved remarkable performance in hyperspectral image (HSI) classification. We propose a spectral graph reasoning network (SGR) learning framework comprising two crucial modules. Experiments on two HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy.
  arXiv  Detail & Related papers  (2024-07-02T20:29:23Z)
• HoloNets: Spectral Convolutions do extend to Directed Graphs [59.851175771106625]
  Conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs. Here we show this traditional reliance on the graph Fourier transform to be superfluous. We provide a frequency-response interpretation of newly developed filters, investigate the influence of the basis used to express filters and discuss the interplay with characteristic operators on which networks are based.
  arXiv  Detail & Related papers  (2023-10-03T17:42:09Z)
• Hodge-Aware Contrastive Learning [101.56637264703058]
  Simplicial complexes prove effective in modeling data with multiway dependencies. We develop a contrastive self-supervised learning approach for processing simplicial data.
  arXiv  Detail & Related papers  (2023-09-14T00:40:07Z)
• SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models [18.391049303136715]
  We propose a generative framework for HSI classification with diffusion models (SpectralDiff) SpectralDiff effectively mines the distribution information of high-dimensional and highly redundant data. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-12T16:32:34Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification [50.899576891296235]
  Convolutional neural networks have been widely applied to hyperspectral image classification. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies.
  arXiv  Detail & Related papers  (2021-06-26T06:24:51Z)
• Spectral Analysis Network for Deep Representation Learning and Image Clustering [53.415803942270685]
  This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis. It can identify the local similarities among images in patch level and thus more robust against occlusion. It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
  arXiv  Detail & Related papers  (2020-09-11T05:07:15Z)
• Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification [5.572542792318872]
  Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. Standard convolutional kernel neglects intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. This paper presents a novel architecture which explicitly addresses these two issues.
  arXiv  Detail & Related papers  (2020-01-20T13:49:43Z)

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.