Graph Adversarial Technology Experiment Log Day 2

Zacong

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Post on Dec 30, 2024

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Graph Adversarial Technology Experiment Log: Day 2

Keywords: Graph Adversarial Network, GAN, Adversarial Training, Graph Neural Network, Anomaly Detection, Experiment Log, Day 2, Results, Challenges, Future Directions

Introduction:

This is the second day of our experiment focusing on the application of Graph Adversarial Networks (GANs) to improve anomaly detection within large, complex graph datasets. Yesterday's log detailed the setup and initial model training. Today's log focuses on the initial results, challenges encountered, and planned adjustments for tomorrow's experimental phase.

Model Performance & Initial Findings (Day 2):

After 24 hours of training, the GAN demonstrated a noticeable improvement in distinguishing between normal and anomalous nodes within our synthetic graph dataset. We utilized a modified version of the Deep Graph Infomax (DGI) architecture as the generator, focusing on preserving the underlying structural properties of the graph. The discriminator, a Graph Convolutional Network (GCN), effectively learned to identify subtle deviations introduced by the generator's adversarial perturbations.

Metrics:

• AUC (Area Under the Curve): We observed a significant increase in AUC from 0.78 (Day 1) to 0.88 on our test set. This indicates improved discrimination between normal and anomalous nodes.
• Precision: Precision increased from 0.75 to 0.82, showing a reduction in false positives.
• Recall: Recall remained relatively stable at 0.85, indicating consistent detection of anomalies.

Challenges Encountered:

Despite the encouraging results, several challenges emerged:

• Mode Collapse: The generator showed signs of mode collapse during certain training epochs. This resulted in a limited diversity of generated anomalies, potentially hindering the discriminator's ability to generalize. We suspect this is related to the discriminator's relatively fast learning rate compared to the generator.
• Computational Cost: Training the GAN on the large graph dataset is computationally expensive. While we are leveraging GPU acceleration, the training time is still considerable, demanding further optimization strategies.

Adjustments & Next Steps (Day 3):

Based on the observations from Day 2, we will implement the following adjustments for Day 3:

• Hyperparameter Tuning: We will experiment with different learning rates for both the generator and discriminator to mitigate mode collapse. We'll also explore different optimizers, such as AdamW, to potentially enhance convergence.
• Regularization Techniques: Implementing regularization techniques like dropout and weight decay to enhance the model's robustness and generalization capabilities.
• Dataset Augmentation: We'll investigate augmenting the training dataset with more diverse and realistic anomaly types to improve the discriminator's ability to identify unseen anomalies. Exploring techniques like edge perturbation and node attribute modification.

Conclusion (Day 2):

Day 2's experiment demonstrated promising results regarding the applicability of GANs for improving anomaly detection within graph data. While challenges persist, the observed improvements in AUC, precision, and recall are encouraging. The next phase will focus on addressing mode collapse and computational cost issues through targeted hyperparameter tuning and the implementation of regularization techniques. A detailed analysis of the generated anomalies will also be conducted to further refine our understanding of the model's strengths and weaknesses. We anticipate further progress and improved performance in subsequent days.