Research Article
UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning
Table 6
Prediction performance of different methods for retweeting behavior (%).
| Methods | Precision | Recall | F1-score | AUC |
| Spatial- & Temporal-level & Handcrafted features + LR (ST&HC+LR) | 69.74 | 71.58 | 70.65 | 77.27 | Spatial- & Temporal-level & Handcrafted features + SVM (ST&HC+SVM) | 68.38 | 69.15 | 68.76 | 78.01 | DeepWalk + ST + GAT | 78.21 | 78.46 | 78.28 | 82.81 | DeepWalk + ST&HC + GAT | 79.68 | 80.24 | 79.96 | 82.75 | Node2vec + ST + GAT | 78.54 | 81.50 | 79.99 | 82.53 | Node2vec + ST&HC + GAT | 79.88 | 81.25 | 80.55 | 82.96 | Our method (UserRBPM) | 81.97 | 82.58 | 82.27 | 83.21 |
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