Practical Solutions for Social Graph Accuracy Improvement
Improving generation accuracy requires three dimensions: data preprocessing, model tuning and validation process:
- data enhancement::
- existGAG_dataCatalog Adding Domain Specific Data
- utilization--user_inputParameters inject a priori knowledge (e.g., "Please model citation relationships in academic circles, authors with high h-index are more likely to be cited") - parameter optimization::
1. AdjustmentstemperatureParameters (0.3-0.7 range more suitable for deterministic scenarios)
2. Modificationsmax_tokensControlling Generation Complexity
3. Ingenerate_argsAdd'top_p': 0.9Balancing diversity
Validation Techniques:
- utilizationevaluatemodule, add the--metric_detailParameters to obtain segmentation metrics
- Compare the degree distribution curve of the real map and adjust the--configThe network parameters in the
- Detecting anomalous connections with Gephi's ForceAtlas2 layout during visualization
Advanced Programs:
1. Fine-tuning the underlying LLM: modificationsagentscopePrompt templates in
2. Integration of real data: the existing mapping is passed through the--data_mixParameter mixing generation
3. Multi-layer validation: running social network and citation network assessment scripts simultaneously for cross-validation
This answer comes from the articleGAG: Generating a Social Relationship Graph Using a Large Model to Simulate Human BehaviorThe
