IEEE

Rethinking Variational Bayes in Community Detection From Graph Signal Perspective

Abstract: Methods based on variational bayes theorytare widely used to detect community structures in networks. In recent years, many related methods have emerged that provide valuable insights into ...
IEEE

Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs

Abstract: Generative self-supervised learning (SSL), especially masked autoencoders (MAE), has greatly succeeded and garnered substantial research interest in graph machine learning. However, the ...
GitHub

Trajectory–User Linking via Heterogeneous Preference Graph and Dual-Encoder Mutual Distillation (HPG-DEMD)

(1) Duration (Days): This represents the total time span of the dataset, calculated as the number of days between the first and the last check-in recorded in the entire dataset. For instance, the ...
Microsoft

Refined graph encoder embedding via self-training and latent community recovery

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within ...
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
GitHub

Variational Autoencoder (VAE) for Image Generation

This project implements a Variational Autoencoder (VAE) for image generation. Unlike standard autoencoders, VAE learns a probabilistic latent space by encoding images to a distribution and sampling ...