Integrate GAT and MAML for enhanced social network analysis and adaptation

[leonvanbokhorst]

Summary by Sourcery

Update the project to include Graph Attention Networks (GAT) for social network analysis and Model-Agnostic Meta-Learning (MAML) for rapid adaptation. Enhance psychological modeling with emotional states and improve documentation to reflect these changes.

New Features:

• Introduce Graph Attention Networks (GAT) for simulating emergent behavior in complex social systems.
• Implement Model-Agnostic Meta-Learning (MAML) for rapid adaptation in simulations.

Enhancements:

• Enhance psychological modeling by incorporating emotional states into individual and group psychology models.
• Improve social network analysis with advanced relationship modeling using quantum-inspired dynamics.

Documentation:

• Update README to reflect new features and enhancements, including GAT and MAML integration.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR updates the project's social network analysis capabilities by introducing Graph Attention Networks (GAT) and emotional states modeling. The changes include a significant restructuring of the documentation, removal of detailed implementation sections, and addition of new core components focusing on GAT and MAML implementations. The PR also updates Python version requirements and simplifies various documentation sections.

Class diagram for updated core components

classDiagram
    class SocialNetworkAnalysis {
        +GraphAttentionNetwork gat
        +MultiHeadAttention multiHeadAttention
        +LouvainMethod communityDetection
        +visualizeSocialDynamics()
        +trackMetrics()
    }

    class MetaLearningFramework {
        +MAMLImplementation maml
        +AdaptiveLearningRate adaptiveLearningRate
        +SkipConnections skipConnections
        +visualizeCapabilities()
        +taskSpecificAdaptation()
    }

    class NarrativeFieldDynamics {
        +StoryWaves storyWaves
        +ThreeStoryEvolution threeStoryEvolution
        +SimpleLabScenario simpleLabScenario
    }

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File-Level Changes

Change	Details	Files
Introduction of Graph Attention Networks (GAT) for social network analysis	Added GAT implementation with multi-head attention mechanisms Integrated community detection using Louvain method Added real-time visualization of social dynamics Implemented comprehensive metrics tracking	src/gat_social_network.py README.md
Integration of Model-Agnostic Meta-Learning (MAML) framework	Added MAML implementation with adaptive learning rate scheduling Implemented skip connections for improved gradient flow Added visualization capabilities Integrated task-specific adaptation features	src/maml_model_agnostic_meta_learning.py README.md
Documentation restructuring and simplification	Removed detailed PoC implementation sections Simplified project structure documentation Updated key features to reflect new GAT and emotional states focus Streamlined development guidelines and CI/CD sections	README.md
Technical requirements update	Updated Python version requirement to 3.12.6 Changed main demo script from nfs_simple_lab_scenario.py to gat_social_network.py	README.md

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[sourcery-ai]

Hey @leonvanbokhorst - I've reviewed your changes - here's some feedback:

Overall Comments:

• Please clarify where the detailed implementation documentation (previously in README.md) has been relocated to. This includes the proof-of-concept implementations, experimental results, and simulation CLI documentation.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟡 Documentation: 2 issues found

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[sourcery-ai]

nitpick (documentation): Inconsistent punctuation in bullet points

Consider maintaining consistency in punctuation across all bullet points - either all with periods or all without.

[sourcery-ai]

issue (documentation): Empty placeholder text in documentation

There are two instances of 'Reference to implementation:' that appear to be placeholder text without actual content. Consider adding the missing references or removing these lines.