1
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
2
ADBench: Anomaly Detection Benchmark
3
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
4
Raising the Bar in Graph-level Anomaly Detection
5
On Equivalence of Anomaly Detection Algorithms
6
PyGOD: A Python Library for Graph Outlier Detection
7
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
8
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
9
Data Augmentation for Deep Graph Learning
10
Towards Similarity-Aware Time-Series Classification
11
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
12
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
13
Higher-order Structure Based Anomaly Detection on Attributed Networks
14
FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance
15
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
16
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
17
An Empirical Study of Graph Contrastive Learning
18
A Synergistic Approach for Graph Anomaly Detection With Pattern Mining and Feature Learning
19
Decoupling Representation Learning and Classification for GNN-based Anomaly Detection
20
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
21
Training Graph Neural Networks with 1000 Layers
22
Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs
23
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
24
Few-shot Network Anomaly Detection via Cross-network Meta-learning
25
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
26
On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights
27
A Large-Scale Database for Graph Representation Learning
28
Generative Adversarial Attributed Network Anomaly Detection
29
Error-Bounded Graph Anomaly Loss for GNNs
30
TODS: An Automated Time Series Outlier Detection System
31
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
32
Next-item Recommendation with Sequential Hypergraphs
33
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
34
Inductive Anomaly Detection on Attributed Networks
35
Policy-GNN: Aggregation Optimization for Graph Neural Networks
36
Open Graph Benchmark: Datasets for Machine Learning on Graphs
37
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data
38
Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions
39
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
40
One-class graph neural networks for anomaly detection in attributed networks
41
Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks
42
Can graph neural networks count substructures?
43
POLITICAL DISCOURSE CONTENT ANALYSIS: A CRITICAL OVERVIEW OF A COMPUTERIZED TEXT ANALYSIS PROGRAM LINGUISTIC INQUIRY AND WORD COUNT (LIWC)
44
Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding
45
Uncovering Coordinated Networks on Social Media
46
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
47
AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN
48
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
49
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
50
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
51
GraphSAINT: Graph Sampling Based Inductive Learning Method
52
Deep Anomaly Detection on Attributed Networks
53
PyOD: A Python Toolbox for Scalable Outlier Detection
54
LSCP: Locally Selective Combination in Parallel Outlier Ensembles
55
Outlier Aware Network Embedding for Attributed Networks
56
Pitfalls of Graph Neural Network Evaluation
57
Deep Structure Learning for Fraud Detection
58
ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks
59
REV2: Fraudulent User Prediction in Rating Platforms
60
Radar: Residual Analysis for Anomaly Detection in Attributed Networks
61
Anomaly Detection with Robust Deep Autoencoders
62
Variational Graph Auto-Encoders
63
Semi-Supervised Classification with Graph Convolutional Networks
64
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
65
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
66
Discovering Opinion Spammer Groups by Network Footprints
67
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
68
Image-Based Recommendations on Styles and Substitutes
69
A Meta-Analysis of the Anomaly Detection Problem
70
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
71
Graph based anomaly detection and description: a survey
72
Auto-Encoding Variational Bayes
73
Statistical Selection of Congruent Subspaces for Mining Attributed Graphs
74
Ranking outlier nodes in subspaces of attributed graphs
75
Isolation-Based Anomaly Detection
76
Non-Negative Residual Matrix Factorization with Application to Graph Anomaly Detection
77
Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms
78
NUS-WIDE: a real-world web image database from National University of Singapore
79
Collective Classification in Network Data
80
SCAN: a structural clustering algorithm for networks
81
Clustering in complex directed networks.
82
Statistical Comparisons of Classifiers over Multiple Data Sets
83
The dynamics of viral marketing
84
A (sub)graph isomorphism algorithm for matching large graphs
85
AutoPart: Parameter-Free Graph Partitioning and Outlier Detection
86
The Enron Corpus: A New Dataset for Email Classification Research
87
LOF: identifying density-based local outliers
88
Contrastive Attributed Network Anomaly Detection with Data Augmentation
89
AutoGML: Fast Automatic Model Selection for Graph Machine Learning
90
Towards Unsupervised HPO for Outlier Detection
91
TOD : T ENSOR -B ASED O UTLIER D ETECTION , A G ENERAL GPU-A CCELERATED F RAMEWORK
92
Automatic Unsupervised Outlier Model Selection
93
Revisiting Time Series Outlier Detection: Definitions and Benchmarks
94
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
95
Count-gnn: Graph neural networks for subgraph isomorphism counting
96
Outlier Detection with Autoencoder Ensembles
97
Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count
98
An Introduction to Outlier Analysis
99
Collective Classi!cation in Network Data
100
Linguistic Inquiry and Word Count (LIWC2007)
101
Evaluation of both detection quality and computational efficiency
102
Extending the scope from static attribute outlier node detection to more graph tasks, e.g., outlier detection in edges and sub-graphs. This will lead to other interesting aspects of graph OD
103
Monitoring and adding outlier node methods to PyGOD for both benchmark and general usage
104
Incorporating automated machine learning to enable intelligent model selection and hyperparameter tuning [59, 93], which may unlock some interesting perspectives of graph OD
105
In the long term, we envision PyGOD could keep evolving to support more comprehensive benchmarking, as well as other graph detection tasks and benchmarks
106
Optimizing its accessibility and scalability with the latest development in graph learning [35], which may bring us new insights into outlier node detection's scalability
