1 INTRODUCTION
1.1 Challenges in GAD with Deep Learning
1.2 Existing Anomaly Detection Surveys
1.3 Contributions
2 PRELIMINARIES
3 ANOMALOUS NODE DETECTION (ANOS ND)
3.1 ANOS ND on Plain Graphs
3.2 ANOS ND on Attributed Graphs
4 ANOS ND ON DYNAMIC GRAPHS
4.1 Network Representation Based Techniques
4.2 GAN Based Techniques
5 ANOMALOUS EDGE DETECTION (ANOS ED)
5.1 Deep NN Based Techniques
5.2 GCN Based Techniques
5.3 Network Representation Based Techniques
6 ANOS ED ON DYNAMIC GRAPHS
6.1 Network Representation Based Techniques
6.2 GCN Based Techniques
7 ANOMALOUS SUB-GRAPH DETECTION (ANOS SGD)
8 ANOMALOUS GRAPH DETECTION (ANOS GD)
8.1 GNN Based Techniques
8.2 Network Representation Based Techniques
9 ANOS GD ON DYNAMIC GRAPHS
10 PUBLISHED ALGORITHMS AND DATASETS
10.1 Published Algorithms
10.2 Published Datasets
10.3 Synthetic Dataset Generation
10.4 Evaluation Metrics
11 FUTURE DIRECTIONS
11.1 Anomalous Edge, Sub-graph, and Graph Detection
11.2 Anomaly Detection in Dynamic Graphs
11.3 Anomaly Detection in Heterogeneous Graphs
11.4 Anomaly Detection in Large-scale Graphs
11.5 Multi-view Graph Anomaly Detection
11.6 Camouflaged/Adversarial Anomaly Detection
11.7 Imbalanced Graph Anomaly Detection
11.8 Multi-task Anomaly Detection
11.9 Graph Anomaly Interpretability
11.10 Graph Anomaly Identification Strategies
11.11 Systematic Benchmarking
11.12 Unified Anomaly Detection Framework
12 CONCLUSION