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 ...
GitHub

DGGVAE: Dual-Granularity Graph Variational Auto-Encoder for Group Recommendation

# For Mafengwo python main.py --dataset=Mafengwo # For CAMRa2011 python main.py --dataset=CAMRa2011 # For Weeplaces python main.py --dataset=Weeplaces ...
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 ...
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 ...