# Week 2: Enhanced Method Implementations Plan

## 📋 **Current Implementation Analysis**

### ✅ **Already Working Well:**
- **Basic data access**: `nodes()`, `edges()`, `size()`, `node_count()`, `edge_count()` - all functional
- **Attribute access**: `get_node_attribute()`, `get_edge_attribute()` - working with parent graph
- **Simple validation**: `contains_node()`, `contains_edge()` - working with direct checks
- **Display methods**: `summary()`, `display_info()` - functional and informative

### 🔄 **Methods Needing Enhancement (Priority Order):**

#### **🏆 Priority 1: Core Functionality**

1. **`table(py: Python) -> PyResult<PyObject>`**
   - **Current**: Returns basic dict with node/edge counts
   - **Target**: Return proper GraphTable with node attributes and data analysis features
   - **Benefit**: Essential for data analysis workflows

2. **`connected_components(py: Python) -> PyResult<Vec<PySubgraph>>`**
   - **Current**: Returns single component containing all nodes  
   - **Target**: Run actual connected components algorithm on subgraph
   - **Benefit**: Core graph analysis functionality

3. **`bfs(py: Python, start_node, max_depth) -> PyResult<PySubgraph>`**
   - **Current**: Returns single-node subgraph
   - **Target**: Actual breadth-first search traversal within subgraph
   - **Benefit**: Essential traversal algorithm

4. **`dfs(py: Python, start_node, max_depth) -> PyResult<PySubgraph>`**
   - **Current**: Returns single-node subgraph  
   - **Target**: Actual depth-first search traversal within subgraph
   - **Benefit**: Essential traversal algorithm

#### **🎯 Priority 2: Advanced Graph Operations**

5. **`shortest_path(py: Python, source, target) -> PyResult<Option<PySubgraph>>`**
   - **Current**: Returns path if both nodes exist
   - **Target**: Compute actual shortest path using parent graph
   - **Benefit**: Critical for path analysis

6. **`degree(py: Python, node_id) -> PyResult<usize>`**
   - **Current**: Simple edge count division
   - **Target**: Actual degree calculation from parent graph
   - **Benefit**: Accurate structural metrics

7. **`neighbors(py: Python, node_id) -> PyResult<Vec<usize>>`**
   - **Current**: Returns all other nodes as neighbors
   - **Target**: Actual neighbors from parent graph within subgraph
   - **Benefit**: Correct adjacency information

#### **🔧 Priority 3: Data Operations**

8. **`filter_nodes(py: Python, query) -> PyResult<PySubgraph>`**
   - **Current**: Simple string contains check on node IDs
   - **Target**: Proper attribute-based filtering with query engine
   - **Benefit**: Powerful data filtering capabilities

9. **`filter_edges(py: Python, query) -> PyResult<PySubgraph>`**
   - **Current**: Simple string contains check on edge IDs  
   - **Target**: Proper attribute-based edge filtering
   - **Benefit**: Edge-based subgraph creation

#### **📊 Priority 4: Network Analysis**

10. **`adjacency_matrix(py: Python) -> PyResult<PyObject>`**
    - **Current**: Using NetworkX conversion (inefficient)
    - **Target**: Direct matrix computation from subgraph structure
    - **Benefit**: Better performance for matrix operations

11. **Graph metrics**: `clustering()`, `transitivity()`, `is_connected()`, `diameter()`
    - **Current**: NetworkX delegation
    - **Target**: Native implementations optimized for subgraphs  
    - **Benefit**: Better performance and integration

## 🚀 **Week 2 Implementation Strategy**

### **Day 1-2: Core Graph Access Pattern**
First, we need to establish the proper way to access PyGraph's internal methods from trait implementations.

**Research PyGraph's public API methods:**
- Find the correct way to access `analytics` module  
- Find the correct way to access `nodes` accessor
- Establish patterns for safe borrow management

### **Day 3-4: Priority 1 Methods**
Implement the 4 most critical methods with full functionality:
1. `table()` - Proper GraphTable creation
2. `connected_components()` - Real algorithm  
3. `bfs()` - Real breadth-first search
4. `dfs()` - Real depth-first search

### **Day 5-6: Priority 2 Methods**  
Enhance the graph operation methods:
5. `shortest_path()` - Real pathfinding
6. `degree()` - Accurate degree calculation
7. `neighbors()` - Correct neighbor lists

### **Day 7: Priority 3 & Testing**
8. Enhanced filtering methods
9. Comprehensive testing of all enhanced methods
10. Performance validation

## 📈 **Expected Outcomes**

By end of Week 2:
- **Functional accuracy**: All core methods work with real algorithms
- **Performance improvement**: Native implementations vs NetworkX delegation  
- **API consistency**: Behavior matches parent graph methods
- **Test coverage**: All enhanced methods fully tested
- **Documentation**: Clear examples of enhanced functionality

## 🎯 **Success Metrics**

- [ ] `table()` returns actual GraphTable with node attributes
- [ ] `connected_components()` correctly identifies disconnected subgraphs
- [ ] `bfs()`/`dfs()` return accurate traversal trees
- [ ] `shortest_path()` finds optimal paths within subgraph
- [ ] `degree()`/`neighbors()` return correct graph structure data
- [ ] All enhanced methods pass comprehensive test suite
- [ ] Performance benchmarks show improvement over simplified versions

This systematic approach ensures we build on our solid Week 1 foundation and create production-ready enhanced implementations.