Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiway geometric degradation. Under an orthogonal Tucker model, we characterize this degradation using two metrics: cross-mode Direction Loss, measuring geometric subspace deviation from rank truncation and noise rotation, and Interaction Loss, quantifying multilinear interaction distortion in the core tensor.
arXiv:2606. 00758v1 Announce Type: cross Abstract: In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals defined on nodes while accounting for their relationships represented by edges.
By Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
arXiv:2507. 02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation.
By Xuesong Zhou, Taehooie Kim, Mostafa Ameli, Henan Zhu, Yudai Honma, Ram M. Pendyala
arXiv:2607. 23338v1 Announce Type: new Abstract: Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored.
By Kawshik Banerjee, Khaled Mohammed Saifuddin
arXiv:2606. 31061v1 Announce Type: cross Abstract: Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data.
By Hiroki Takeda, Yuto Miyatake, Daisuke Furihata
arXiv:2607. 25295v2 Announce Type: replace Abstract: Tensorial multi-view clustering (TMC) has achieved strong performance due to its ability to capture high-order correlations across multiple views.
By Jintian Ji, Xingsu Li, Songhe Feng
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observations.
arXiv:2606. 27455v1 Announce Type: cross Abstract: We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph.
By Rasoul Shafipour, Andrei Buciulea, Santiago Segarra, Antonio G. Marques, Gonzalo Mateos
The paper introduces Spectral Connectivity-Regularized Graph Learning (SCoGL), a method for learning sparse graphs from limited data by incorporating Laplacian spectral priors that promote global connectivity. SCoGL extends the graphical lasso objective with a connectivity prior derived from Laplacian eigenvalues and uses projected gradient descent with Armijo backtracking for optimization. Experiments demonstrate that SCoGL improves graph recovery and enhances downstream tasks such as graph signal denoising when observations are scarce.
By Mingxiao Liu (Tsinghua University, China), Bahar Oveisgharan (York University, Canada), Bingyan Zou (Tsinghua University, China), Gene Cheung (York University, Canada), H. Vicky Zhao (Tsinghua University, China), Feifei Gao (Tsinghua University, China)
The paper introduces Ricci-Diffusion, a graph denoising technique that uses curvature-guided diffusion inspired by Ricci flow. Unlike traditional similarity-driven methods, it modulates local transport in the diffusion kernel based on edge-level curvature, steering edge-weight updates toward a more regular graph geometry. The authors provide theoretical analysis showing curvature’s ability to distinguish graph structures and induce first-order corrections, and demonstrate that the method converges to a stable denoised network, improving structure recovery and downstream performance on real-world and synthetic graphs.
By Ye Fang, Chuan-Xian Ren
arXiv:2607. 21263v1 Announce Type: new Abstract: Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice.
By Purui Zhang, Feng Ji, Yanan Zhao, Bihan Wen, Wee Peng Tay
arXiv:2410. 08238v2 Announce Type: replace-cross Abstract: We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks.
By F\'elix Marcoccia, Victor Fagoo, Gilles Monzat, C\'edric Adjih, Thomas Watteyne, Paul M\"uhlethaler