IEEE

Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud

Abstract: Many practical problems can be formulated as graph-based semi-supervised classification problems. For example, online finance anti-fraud. Recently, many researchers attempt using deep ...
IEEE

3D Human Pose Estimation with Two-Step MixedGraph Convolution Transformer Encoder

Abstract: In 3D human pose estimation, human joints typically exhibit continuity and interdependence during motion, suggesting that joint velocities and their positional relationships can offer ...
INSPIRE

Design and evaluation of a quantum variational graph auto-encoder using quantum graph neural networks

Quantum Variational Graph Auto-Encoders (QVGAE) represent an integration of graph-based machine learning and quantum computing. In this work, we propose a first-of-its-kind quantum implementation of ...
GitHub

Code Repository for Paper "Pretraining Graph State Encoders for microRTS using Graph Self-Supervised Learning"

Code repository for workshop paper "Pretraining Graph State Encoders for microRTS using Graph Self-Supervised Learning" - Teravolt/microrts-graph-state-encoder-gssl ...
Microsoft

xGCN An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...
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 ...