Interface IGraphConvolutionLayer<T>

Namespace

AiDotNet.Interfaces

Assembly

AiDotNet.dll

Defines the contract for graph convolutional layers that process graph-structured data.

public interface IGraphConvolutionLayer<T> : ILayer<T>, IJitCompilable<T>, IDiagnosticsProvider, IWeightLoadable<T>

Type Parameters

T

The numeric type used for calculations, typically float or double.

Inherited Members

ILayer<T>.GetInputShape()

ILayer<T>.GetOutputShape()

ILayer<T>.GetWeights()

ILayer<T>.GetBiases()

ILayer<T>.Forward(Tensor<T>)

ILayer<T>.ForwardGpu(params IGpuTensor<T>[])

ILayer<T>.CanExecuteOnGpu

ILayer<T>.Backward(Tensor<T>)

ILayer<T>.UpdateParameters(T)

ILayer<T>.UpdateParameters(Vector<T>)

ILayer<T>.ParameterCount

ILayer<T>.Serialize(BinaryWriter)

ILayer<T>.Deserialize(BinaryReader)

ILayer<T>.GetActivationTypes()

ILayer<T>.GetParameters()

ILayer<T>.SupportsTraining

ILayer<T>.SetTrainingMode(bool)

ILayer<T>.GetParameterGradients()

ILayer<T>.ClearGradients()

ILayer<T>.SetParameters(Vector<T>)

ILayer<T>.ResetState()

ILayer<T>.SupportsGpuTraining

ILayer<T>.UpdateParametersGpu(IGpuOptimizerConfig)

ILayer<T>.UploadWeightsToGpu()

ILayer<T>.DownloadWeightsFromGpu()

ILayer<T>.ZeroGradientsGpu()

IJitCompilable<T>.ExportComputationGraph(List<ComputationNode<T>>)

IJitCompilable<T>.SupportsJitCompilation

IDiagnosticsProvider.GetDiagnostics()

IWeightLoadable<T>.GetParameterNames()

IWeightLoadable<T>.TryGetParameter(string, out Tensor<T>)

IWeightLoadable<T>.SetParameter(string, Tensor<T>)

IWeightLoadable<T>.GetParameterShape(string)

IWeightLoadable<T>.NamedParameterCount

IWeightLoadable<T>.ValidateWeights(IEnumerable<string>, Func<string, string>)

IWeightLoadable<T>.LoadWeights(Dictionary<string, Tensor<T>>, Func<string, string>, bool)

Remarks

Graph convolutional layers process data that is organized as graphs (nodes and edges). This interface extends the base layer interface with graph-specific functionality, particularly the ability to work with adjacency matrices that define graph structure.

For Beginners: This interface defines what all graph layers must be able to do.

Graph layers are special because they work with data that has connections:

• Social networks (people connected to friends)
• Molecules (atoms connected by bonds)
• Transportation networks (cities connected by roads)
• Knowledge graphs (concepts connected by relationships)

The key difference from regular layers is that graph layers need to know which nodes are connected to which other nodes. That's what the adjacency matrix provides.

Properties

InputFeatures

Gets the number of input features per node.

int InputFeatures { get; }

Property Value

int

Remarks

This property indicates how many features each node in the graph has as input. For example, in a molecular graph, this might be properties of each atom.

For Beginners: This tells you how many pieces of information each node starts with.

Examples:

• In a social network: age, location, interests (3 features)
• In a molecule: atomic number, charge, mass (3 features)
• In a citation network: word embeddings (300 features)

Each node has the same number of input features.

OutputFeatures

Gets the number of output features per node.

int OutputFeatures { get; }

Property Value

int

Remarks

This property indicates how many features each node will have after processing through this layer. The layer transforms each node's input features into output features through learned transformations.

For Beginners: This tells you how many pieces of information each node will have after processing.

The layer learns to:

• Combine input features in useful ways
• Extract important patterns
• Create new representations that are better for the task

For example, if you start with 10 features per node and the layer has 16 output features, each node's 10 numbers will be transformed into 16 numbers that hopefully capture more useful information for your specific task.

Methods

GetAdjacencyMatrix()

Gets the adjacency matrix currently being used by this layer.

Tensor<T>? GetAdjacencyMatrix()

Returns

Tensor<T>

The adjacency matrix tensor, or null if not set.

Remarks

This method retrieves the adjacency matrix that was set using SetAdjacencyMatrix. It may return null if the adjacency matrix has not been set yet.

For Beginners: This method lets you check what graph structure the layer is using.

This can be useful for:

• Verifying the correct graph was loaded
• Debugging graph connectivity issues
• Visualizing the graph structure

SetAdjacencyMatrix(Tensor<T>)

Sets the adjacency matrix that defines the graph structure.

void SetAdjacencyMatrix(Tensor<T> adjacencyMatrix)

Parameters

adjacencyMatrix Tensor<T>

The adjacency matrix tensor representing node connections.

Remarks

The adjacency matrix is a square matrix where element [i,j] indicates whether and how strongly node i is connected to node j. Common formats include: - Binary adjacency: 1 if connected, 0 otherwise - Weighted adjacency: connection strength as a value - Normalized adjacency: preprocessed for better training

For Beginners: This method tells the layer how nodes in the graph are connected.

Think of the adjacency matrix as a map:

• Each row represents a node
• Each column represents a potential connection
• The value at position [i,j] tells if node i connects to node j

For example, in a social network:

• adjacencyMatrix[Alice, Bob] = 1 means Alice is friends with Bob
• adjacencyMatrix[Alice, Charlie] = 0 means Alice is not friends with Charlie

This connectivity information is crucial for graph neural networks to propagate information between connected nodes.