Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
GitHub

Learning to Slice: Self-Supervised Interpretable Hierarchical Representation Learning with Graph Auto-Encoder Tree

We support any dataset collected with Twitter (now X) official API, which is in the format of jsonl (see https://developer.x.com/en/docs/x-api). data_preprocess.py ...
C&EN

MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug–Drug Interaction Prediction

School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China ...
marktechpost

Hierarchical Graph Masked AutoEncoders (Hi-GMAE): A Novel Multi-Scale GMAE Framework Designed to Handle the Hierarchical Structures within Graph

In graph analysis, the need for labeled data presents a significant hurdle for traditional supervised learning methods, particularly within academic, social, and biological networks. To overcome this ...
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 ...
C&EN

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Molecular generation is crucial for advancing drug discovery, materials science, and ...
IEEE

Reconstructed Graph Constrained Auto-Encoders for Multi-View Representation Learning

Abstract: The application of Auto-Encoder (AE) to multi-view representation learning has gained traction due to advancements in deep learning. While some current AE-based multi-view representation ...
IEEE

Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding

Abstract: Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph ...
Europe PMC

Structure enhanced deep clustering network via a weighted neighbourhood auto-encoder.

Structural deep clustering involves the use of neural networks for fusing semantic and structural representations for clustering tasks, and it has been receiving increasing attention. In some ...