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22 papers · 1 filter
Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion
Xuling Zhang, Peng Wang, Daiyan Li +3
Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors. In many real-world graphs, edges c…
Automated Learning of Semantic Embedding Representations for Diffusion Models
Limai Jiang, Yunpeng Cai
Generative models capture the true distribution of data, yielding semantically rich representations. Denoising diffusion models (DDMs) exhibit superior generative capabilities, tho…
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
Xuewen Dong, Jiachen Li, Shujun Li +4
Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trig…
Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexity
Yixin Ren, Haocheng Zhang, Yewei Xia +3
Score-based causal discovery methods can effectively identify causal relationships by evaluating candidate graphs and selecting the one with the highest score. One popular class of…
Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability
Yixuan Wang, Zhenwu Chen, Kangshuai Zhang +3
With the sharp increase in the number of vehicles, the issue of parking difficulties has emerged as an urgent challenge that many cities need to address promptly. In the task of pr…
Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
Sota Kudo, Naoaki Ono, Shigehiko Kanaya +1
Information Bottleneck (IB) is a widely used framework that enables the extraction of information related to a target random variable from a source random variable. In the objectiv…