feat(deps): add torch-geometric for graph neural network support

[leonvanbokhorst]

Added torch-geometric package to requirements.txt to enable Graph Attention Network (GAT) functionality for modeling agent relationships and social dynamics.

Summary by Sourcery

Add support for Graph Neural Networks by integrating the torch-geometric package and implementing a module for relationship graph analysis using Graph Attention Networks.

New Features:

• Introduce Graph Attention Network (GAT) functionality for modeling agent relationships and social dynamics using the torch-geometric package.

Enhancements:

• Add a new module for managing and analyzing agent relationships using PyTorch Geometric, including features for computing social dynamics, analyzing communities, and visualizing relationship graphs.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR introduces Graph Attention Network (GAT) support by adding torch-geometric as a dependency and implementing a comprehensive relationship graph analysis system. The implementation uses GAT to model and analyze social dynamics between agents, featuring multi-head attention mechanisms, community detection, and influence path analysis.

Sequence diagram for adding an agent and updating relationships

sequenceDiagram
    participant User
    participant RelationshipGraph
    participant AgentNode
    User->>RelationshipGraph: create RelationshipGraph
    User->>AgentNode: create AgentNode
    User->>RelationshipGraph: add_agent(AgentNode)
    RelationshipGraph->>RelationshipGraph: add_node to nx_graph
    User->>RelationshipGraph: update_relationship(source_id, target_id, trust, influence, familiarity)
    RelationshipGraph->>RelationshipGraph: add_edge to nx_graph

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Sequence diagram for computing social dynamics

sequenceDiagram
    participant User
    participant RelationshipGraph
    User->>RelationshipGraph: compute_social_dynamics()
    RelationshipGraph->>RelationshipGraph: _get_node_features()
    RelationshipGraph->>RelationshipGraph: _get_edge_features()
    RelationshipGraph->>GATConv: process data
    GATConv-->>RelationshipGraph: node_embeddings
    RelationshipGraph-->>User: return node_embeddings

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Class diagram for RelationshipGraph and AgentNode

classDiagram
    class AgentNode {
        +String agent_id
        +Dict attributes
        +Dict state
    }
    class RelationshipGraph {
        +int hidden_dim
        +int num_heads
        +String device
        +DiGraph nx_graph
        +Linear node_encoder
        +Linear edge_encoder
        +GATConv gat_layer
        +add_agent(AgentNode agent)
        +update_relationship(String source_id, String target_id, float trust, float influence, float familiarity)
        +compute_social_dynamics() Tensor
        +analyze_communities() List
        +get_influence_paths(String source_id, String target_id) List
        +visualize(String title)
    }
    AgentNode --> RelationshipGraph : used by

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File-Level Changes

Change	Details	Files
Added torch-geometric dependency for graph neural network support	Added torch-geometric package to Python dependencies	requirements.txt
Implemented a Graph Attention Network (GAT) based relationship analysis system	Created RelationshipGraph class with GAT architecture for processing agent relationships Implemented node and edge feature encoding for graph processing Added multi-head attention mechanism with configurable hidden dimensions Implemented community detection using Louvain method Added influence path analysis between agents Created visualization functionality for relationship graphs Implemented a comprehensive demo with classroom social dynamics example	src/14_graph_attention_network.py

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[sourcery-ai]

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

Overall Comments:

• Consider breaking this large PR into smaller, focused changes - e.g. separate PRs for adding the dependency, implementing the core GAT functionality, and adding visualization features.
• Missing test coverage for the new Graph Attention Network implementation. Please add unit tests covering the key functionality including relationship graph operations and community detection.

Here's what I looked at during the review

• 🟡 General issues: 3 issues found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 2 issues found
• 🟢 Documentation: all looks good

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