Table 4 Summary of GraphSAGE Network with Application area, technique, datasets used, and performance measure (accuracy)
Refs. | Application Area | Method Applied | Dataset | Accuracy |
|---|---|---|---|---|
[48] (2017) | Citation Network | GraphSAGE Mean- aggregator | Citation | 77.8% |
GraphSAGE-LSTM aggregator | Citation | 78.8% | ||
GraphSAGE-pool aggregator | Citation | 79.8% | ||
[31] (2019) | Node Classification | 4-layer GCN | Cora, Citeseer, PubMed | 32.20% 53.60% 47.90% 96.47 |
[57] (2019) | Social Network Analysis Based on Graph SAGE | GraphSAGE (GCN) | microblogs | 53.87% |
[58] (2021) | Intrusion Detection | E-GraphSAGE E-ResGAT | UNSW-NB15 CIC-DarkNet CSE-CIC-IDS ToN-IoT | 0.9868 0.8093 0.8774 0.9384 |
[59] (2019) | Data-Driven Node Sampling | GraphSAGE | PPI Reddit PubMed | 0.813 0.954 0.898 |
[60] (2023) | Underwater Moving Object Detection | GraphSAGE +  Aggregator( Mean, Max and LSTM) | Fish4Knowledge dataset | Mean: 98.51% Max: 94.46% LSTM: 98.50% |