This paper introduces Spectral Attention and Graph Convolutional Attention (GCA) for denoising graphs, which outperforms linear attention and provably utilizes the input graph spectrum.

This paper proposes Mem-GF, a memory-efficient graph filtering-based collaborative filtering method that approximates polynomial graph filters using Krylov subspaces, achieving significant memory savi…

This paper addresses the problem of inferring a directed network from nodal measurements using graph convolutional filters and identifies the diffusion filter and network topology.

This paper investigates the use of spectral filtering for continuous subgraph matching over dynamic graphs and presents three key findings.

This paper models the changes in graph adjacency matrices over time as a low-rank matrix and develops a method for jointly interpolating signals and estimating graph adjacency matrices using this assu…

A new solver-free parallel spectral sparsification algorithm for weighted graphs is presented, relying on low-diameter decompositions and independent sampling, eliminating dependence on target approxi…

This paper proposes a fairness-aware adaptation of graph-based diffusion methods by modifying the Laplacian operator to mitigate bias-related components.

The paper investigates the relationship between graph classes, edit cost functions, and computational complexity of the graph edit distance, providing polynomial-time reductions and correspondences.

The paper proposes FedLAB, a traceable semantic codebook framework for federated multimodal graph foundation learning, which organizes multimodal graph knowledge into hierarchical codebooks and refine…

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

The paper introduces a hybrid WGAN-GA framework that uses a Genetic Algorithm (GA) to refine graphs generated by a GAN, significantly reducing structural deviations and improving realism.

This paper introduces DiPhon, a diffusion framework for size-scalable graph generation, using a continuous diffusion process on the graphon space and a discretized graph-level process.

This paper proposes G2Rec, a scalable framework for industrial-scale generative recommendation that unifies graph-based user co-engagement modeling and semantic tokenization.

肖代替了视觉令牌的永久删除，通过可恢复的路由来改进视觉语言模型的性能

This paper studies the triangle-densest-$k$-subgraph problem in an average-case model and proposes spectral and semidefinite algorithms for its recovery.

PrunePath introduces a budget-adaptive structured sparsification framework that efficiently prunes Feed-forward networks in large language models, achieving hardware-friendly sparsity and measurable s…