The paper introduces Graph Cascades, a mesoscopic rewiring technique that enhances Graph Neural Networks by promoting node pairs with strong multi-hop connections to direct edges, improving performanc…

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

The paper introduces a diffusion-based uncertainty model for robust optimization on graphs, showing that the resulting computational complexity depends critically on the interaction between the uncert…

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

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.

The paper proposes a semi-relaxed Gromov-Wasserstein objective to estimate the latent connectivity structure of large-scale networks, achieving statistically consistent and efficient recovery of the u…

This paper proposes a scalable topological learning framework for higher-order graph representation by introducing simplified and factored cellular Weisfeiler Leman tests and a novel random walk metho…

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.

The paper proposes GRiD, a novel framework that uses a two-phase training strategy (supervised pre-training and RL fine-tuning) to discover complex, graph-like rules for knowledge graph reasoning, ove…

This paper proposes DAPGNet, a dynamic adaptive physics-guided graph diffusion network for hyperspectral image classification, which achieves state-of-the-art performance on four datasets.

This paper investigates the effects of structural and temporal inhomogeneities in decentralised federated learning and shows that they significantly slow down the convergence process.

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

This paper analyzes the decoding process of masked diffusion models for graph-to-text generation, finding that structural fine-tuning disrupts natural entity-first generation and proposing a structura…

This paper studies the depth required for a graph neural network on a sparse graph using the contextual stochastic block model, and proves that the error is governed by the Kesten-Stigum ratio.

The paper proposes GC-MoE, a graph-conditioned Mixture of Experts framework, to improve traffic forecasting by assigning personalized, specialized forecasting experts to individual road segments.

This paper presents four novel parallel algorithms for maintaining a rooted spanning forest in dynamic graphs, achieving a throughput of 2 million insertions and 1.4 million deletions per second.

This paper critically re-evaluates the use of Graph Neural Networks (GNNs) for Bitcoin fraud detection, demonstrating that under strict, leakage-free temporal evaluation, simple feature-only models si…