GitHub

zihao-cpu/Graph-Auto-Encoders

Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. GAEs are based on Graph Convolutional Networks (GCNs), a ...
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 ...
IEEE

Masked Auto-Encoder with Multiple Masks for Graph Representation Learning

Abstract: Graph representation is an indispensable technique in the field of E-Business Engineering, as it plays a pivotal role in capturing the underlying structure of various data types prevalent in ...
GitHub

Crystal Graph Auto-Encoder

. ├── M_FEATURE_TABLE.pt ├── README.md ├── cif-files │ ├── test │ └── train ├── compressed_test.pt ├── compressed_train.pt ├── dataset.py ├── edge_bce.png ├── edge_feat.png ├── e ...
IEEE

VQ-VGAE: Vector Quantized Variational Graph Auto-Encoder for Unsupervised Anomaly Detection

Abstract: Detecting anomalies in graph-structured data is critical for identifying unusual patterns within complex systems, with applications spanning cybersecurity, fraud detection, and risk ...
ITU

Unsupervised representation learning for BGP anomaly detection using graph auto-encoders

The Border Gateway Protocol (BGP) is crucial for the communication routes of the Internet. Anomalies in BGP can pose a threat to the stability of the Internet. These anomalies, caused by a variety of ...
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 ...