Fix Issue 401 Error #448

ooples · 2025-11-08T20:19:37Z

This commit implements three critical Graph Neural Network architectures to expand the AiDotNet library's GNN capabilities:

Graph Attention Networks (GAT)
- Multi-head attention mechanism for graph-structured data
- Learns importance weights for neighbor nodes dynamically
- Supports configurable attention heads and dropout
- File: GraphAttentionLayer.cs
GraphSAGE (Graph Sample and Aggregate)
- Inductive learning framework for graph data
- Three aggregator types: Mean, MaxPool, and Sum
- L2 normalization support for stable training
- Designed for generalizing to unseen nodes
- File: GraphSAGELayer.cs
Graph Isomorphism Network (GIN)
- Provably as powerful as Weisfeiler-Lehman test
- Uses sum aggregation with learnable epsilon parameter
- Two-layer MLP for powerful feature transformation
- Optimal for tasks requiring fine-grained structural understanding
- File: GraphIsomorphismLayer.cs

Additional Changes:

Created IGraphConvolutionLayer interface defining common GNN contracts
Moved GraphConvolutionalLayer to new Graph/ subdirectory
Updated GraphConvolutionalLayer to implement IGraphConvolutionLayer
Added comprehensive unit tests for all graph layers (GraphLayerTests.cs)

Architecture:
All layers follow consistent patterns:

Implement IGraphConvolutionLayer and extend LayerBase
Support adjacency matrix input via SetAdjacencyMatrix()
Provide InputFeatures and OutputFeatures properties
Include detailed XML documentation with beginner-friendly explanations

Use Cases:

Social network analysis
Molecular property prediction
Knowledge graph reasoning
Recommendation systems
Citation network analysis

Resolves #401

User Story / Context

Reference: [US-XXX] (if applicable)
Base branch: merge-dev2-to-master

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This commit implements three critical Graph Neural Network architectures to expand the AiDotNet library's GNN capabilities: 1. **Graph Attention Networks (GAT)** - Multi-head attention mechanism for graph-structured data - Learns importance weights for neighbor nodes dynamically - Supports configurable attention heads and dropout - File: GraphAttentionLayer.cs 2. **GraphSAGE (Graph Sample and Aggregate)** - Inductive learning framework for graph data - Three aggregator types: Mean, MaxPool, and Sum - L2 normalization support for stable training - Designed for generalizing to unseen nodes - File: GraphSAGELayer.cs 3. **Graph Isomorphism Network (GIN)** - Provably as powerful as Weisfeiler-Lehman test - Uses sum aggregation with learnable epsilon parameter - Two-layer MLP for powerful feature transformation - Optimal for tasks requiring fine-grained structural understanding - File: GraphIsomorphismLayer.cs **Additional Changes:** - Created IGraphConvolutionLayer<T> interface defining common GNN contracts - Moved GraphConvolutionalLayer to new Graph/ subdirectory - Updated GraphConvolutionalLayer to implement IGraphConvolutionLayer - Added comprehensive unit tests for all graph layers (GraphLayerTests.cs) **Architecture:** All layers follow consistent patterns: - Implement IGraphConvolutionLayer<T> and extend LayerBase<T> - Support adjacency matrix input via SetAdjacencyMatrix() - Provide InputFeatures and OutputFeatures properties - Include detailed XML documentation with beginner-friendly explanations **Use Cases:** - Social network analysis - Molecular property prediction - Knowledge graph reasoning - Recommendation systems - Citation network analysis Resolves #401

coderabbitai · 2025-11-08T20:19:46Z

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📥 Commits

Reviewing files that changed from the base of the PR and between f99b0d2 and 705c5d6.

📒 Files selected for processing (6)

src/NeuralNetworks/Layers/Graph/GraphAttentionLayer.cs (1 hunks)
src/NeuralNetworks/Layers/Graph/GraphConvolutionalLayer.cs (5 hunks)
src/NeuralNetworks/Layers/Graph/GraphIsomorphismLayer.cs (1 hunks)
src/NeuralNetworks/Layers/Graph/GraphSAGELayer.cs (1 hunks)
src/NeuralNetworks/Layers/Graph/IGraphConvolutionLayer.cs (1 hunks)
tests/AiDotNet.Tests/UnitTests/NeuralNetworks/Layers/GraphLayerTests.cs (1 hunks)

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Post copyable unit tests in a comment
Commit unit tests in branch claude/fix-issue-401-011CUw2PdjV8WHcpjT2wAkpr

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Copilot

Pull Request Overview

This PR adds comprehensive support for Graph Neural Networks (GNNs) to the AiDotNet library by introducing a common interface and four distinct graph layer implementations for processing graph-structured data.

Defines IGraphConvolutionLayer<T> interface with methods for managing adjacency matrices and node features
Implements four graph layer types: GCN (Graph Convolutional Network), GAT (Graph Attention Network), GraphSAGE, and GIN (Graph Isomorphism Network)
Provides extensive test coverage for all layers including constructor tests, forward pass validation, and interface compliance checks

Reviewed Changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
IGraphConvolutionLayer.cs	Defines interface contract for graph layers with adjacency matrix management and feature dimensions
GraphConvolutionalLayer.cs	Updates existing GCN layer to implement new interface and adds GetAdjacencyMatrix method
GraphAttentionLayer.cs	New implementation of GAT layer with multi-head attention mechanism
GraphSAGELayer.cs	New implementation of GraphSAGE with support for Mean, MaxPool, and Sum aggregators
GraphIsomorphismLayer.cs	New implementation of GIN layer with learnable epsilon and MLP transformation
GraphLayerTests.cs	Comprehensive unit tests covering all graph layers and their functionality

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Copilot · 2025-11-08T20:24:50Z

src/NeuralNetworks/Layers/Graph/GraphSAGELayer.cs

+/// - Mean: Average all neighbor features (balanced, smooth)
+/// - Max: Take the maximum value from neighbors (emphasizes outliers)
+/// - Sum: Add up all neighbor features (sensitive to number of neighbors)
+/// - LSTM: Use a recurrent network to aggregate (most expressive but slowest)


The documentation mentions LSTM as an aggregation option, but the SAGEAggregatorType enum (lines 15-31) does not include an LSTM option. Either add the LSTM aggregator type to the enum or remove this line from the documentation.

Copilot · 2025-11-08T20:24:50Z

src/NeuralNetworks/Layers/Graph/GraphSAGELayer.cs

+/// <para><b>For Beginners:</b> These are different ways to combine information from neighbors.
+///
+/// - Mean: Average all neighbor features (balanced, smooth)
+/// - Max: Take the maximum value from neighbors (emphasizes outliers)


Corrected 'Max' to 'MaxPool' to match the enum value name.

Suggested change

/// - Max: Take the maximum value from neighbors (emphasizes outliers)

/// - MaxPool: Take the maximum value from neighbors (emphasizes outliers)

Copilot · 2025-11-08T20:24:51Z

src/NeuralNetworks/Layers/Graph/GraphIsomorphismLayer.cs

+        // Epsilon (if learnable)
+        if (_learnEpsilon)
+        {
+            parameters[index++] = _epsilon;


This assignment to index is useless, since its value is never read.

Copilot · 2025-11-08T20:24:51Z

src/NeuralNetworks/Layers/Graph/GraphIsomorphismLayer.cs

+        // Set epsilon (if learnable)
+        if (_learnEpsilon)
+        {
+            _epsilon = parameters[index++];


This assignment to index is useless, since its value is never read.

Suggested change

_epsilon = parameters[index++];

_epsilon = parameters[index];

Copilot · 2025-11-08T20:24:51Z

src/NeuralNetworks/Layers/Graph/GraphAttentionLayer.cs

+    /// <summary>
+    /// Weight matrices for each attention head. Shape: [numHeads, inputFeatures, outputFeatures].
+    /// </summary>
+    private Tensor<T> _weights;


Field '_weights' can be 'readonly'.

Suggested change

private Tensor<T> _weights;

private readonly Tensor<T> _weights;

Copilot · 2025-11-08T20:24:51Z

src/NeuralNetworks/Layers/Graph/GraphAttentionLayer.cs

+    /// <summary>
+    /// Attention mechanism parameters for each head. Shape: [numHeads, 2 * outputFeatures].
+    /// </summary>
+    private Matrix<T> _attentionWeights;


Field '_attentionWeights' can be 'readonly'.

Suggested change

private Matrix<T> _attentionWeights;

private readonly Matrix<T> _attentionWeights;

Copilot · 2025-11-08T20:24:52Z

src/NeuralNetworks/Layers/Graph/GraphAttentionLayer.cs

+    /// <summary>
+    /// Bias vector for the output transformation.
+    /// </summary>
+    private Vector<T> _bias;


Field '_bias' can be 'readonly'.

Suggested change

private Vector<T> _bias;

private readonly Vector<T> _bias;

Copilot · 2025-11-08T20:24:52Z

src/NeuralNetworks/Layers/Graph/GraphAttentionLayer.cs

+                        if (!NumOps.Equals(_adjacencyMatrix[b, i, j], NumOps.Zero))
+                        {
+                            _lastAttentionCoefficients[b, h, i, j] =
+                                NumOps.Divide(_lastAttentionCoefficients[b, h, i, j], sumExp);
+                        }
+                        else
+                        {
+                            _lastAttentionCoefficients[b, h, i, j] = NumOps.Zero;
+                        }


Both branches of this 'if' statement write to the same variable - consider using '?' to express intent better.

Suggested change

if (!NumOps.Equals(_adjacencyMatrix[b, i, j], NumOps.Zero))

{

_lastAttentionCoefficients[b, h, i, j] =

NumOps.Divide(_lastAttentionCoefficients[b, h, i, j], sumExp);

}

else

{

_lastAttentionCoefficients[b, h, i, j] = NumOps.Zero;

}

_lastAttentionCoefficients[b, h, i, j] =

!NumOps.Equals(_adjacencyMatrix[b, i, j], NumOps.Zero)

? NumOps.Divide(_lastAttentionCoefficients[b, h, i, j], sumExp)

: NumOps.Zero;

Copilot AI review requested due to automatic review settings November 8, 2025 20:19

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Fix Issue 401 Error #448

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	/// - Max: Take the maximum value from neighbors (emphasizes outliers)
	/// - MaxPool: Take the maximum value from neighbors (emphasizes outliers)

	_epsilon = parameters[index++];
	_epsilon = parameters[index];

	private Tensor<T> _weights;
	private readonly Tensor<T> _weights;

	private Matrix<T> _attentionWeights;
	private readonly Matrix<T> _attentionWeights;

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Fix Issue 401 Error #448

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Fix Issue 401 Error #448

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