Graph-Anomaly-Loss
Graph Anomaly Detector
A software package for detecting anomalies in graphs by learning patterns and features
TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly Loss for GNNs.
40 stars
4 watching
11 forks
Language: Python
last commit: over 1 year ago
Linked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
 pygod-team/pygod | Detects anomalies in graph data using various algorithms | 1,336 |
 eaglelab-zju/dgld | An open-source library for training and evaluating graph anomaly detection models | 64 |
 yingtongdou/care-gnn | An implementation of a graph neural network-based fraud detector designed to counter camouflaged fraudsters | 246 |
 arundo/adtk | A toolkit for rule-based and unsupervised anomaly detection in time series data | 1,101 |
 orobix/visual-feature-attribution-using-wasserstein-gans-pytorch | A PyTorch implementation of a feature attribution technique using Wasserstein Generative Adversarial Networks for anomaly detection in medical images. | 93 |
 datamllab/tods | An automated system for detecting outliers in time-series data using machine learning algorithms and human expertise. | 1,459 |
 hongyanggao/lgcn | An implementation of learnable graph convolutional networks for efficient graph processing | 46 |
 chanyn/hkrm | Develops a deep learning model for large-scale object detection that leverages hybrid knowledge and routing mechanisms. | 104 |
| datamllab/pyodds | An end-to-end outlier detection system that integrates machine learning algorithms with database support | 252 |
 safe-graph/dgfraud | A toolbox for building and comparing graph neural network-based fraud detection models | 693 |
 muhanzhang/dgcnn | A deep learning architecture for graph classification that extracts vertex features through propagation-based graph convolution and retains more node information than traditional sum pooling methods. | 174 |
 nathanielc/morgoth | An anomaly detection framework for flexible and efficient metric analysis | 280 |
 benedekrozemberczki/sgcn | An implementation of a deep learning algorithm for graph data | 268 |
 hendrycks/outlier-exposure | Anomaly detection framework utilizing out-of-distribution data to improve deep learning model performance. | 548 |
 jingzhang617/cod-rank-localize-and-segment | Develops a system to detect, segment, and rank camouflaged objects in images. | 74 |