11 citations · 11 across the 5 of their papers we have counts for
5 papers
SP-GCRL: Influence Maximization on Incomplete Social Graphs
Haohua Niu, Yuxuan Yang, Lingfeng Zhang +4
Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics. We propose SP-GCRL, a social-propagation-aware…
An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
Lingfeng Zhang, Taoyong Cui, Dongzhan Zhou +6
Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machi…
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
Chenyu Liu, Haige Li, Luca Rossi
Graph Neural Networks (GNNs) are neural networks that aim to process graph data, capturing the relationships and interactions between nodes using the message-passing mechanism. GNN…
Generating Graphs via Spectral Diffusion
Giorgia Minello, Alessandro Bicciato, Luca Rossi +2
In this paper, we present GGSD, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we pro…
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
Alessandro Bicciato, Luca Cosmo, Giorgia Minello +2
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that sub…