Table of Contents

Class EdgeConditionalConvolutionalLayer<T>

Namespace
AiDotNet.NeuralNetworks.Layers
Assembly
AiDotNet.dll

Implements Edge-Conditioned Convolution for incorporating edge features in graph convolutions.

public class EdgeConditionalConvolutionalLayer<T> : LayerBase<T>, IDisposable, IGraphConvolutionLayer<T>, ILayer<T>, IJitCompilable<T>, IDiagnosticsProvider, IWeightLoadable<T>

Type Parameters

T

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

Inheritance
EdgeConditionalConvolutionalLayer<T>
Implements
Inherited Members

Remarks

Edge-Conditioned Convolutions extend standard graph convolutions by incorporating edge features into the aggregation process. Instead of treating all edges equally, this layer learns edge-specific transformations based on edge attributes.

The layer computes: h_i' = σ(Σ_{j∈N(i)} θ(e_ij) · h_j + b) where θ(e_ij) is an edge-specific transformation learned from edge features e_ij.

For Beginners: This layer lets connections (edges) have their own properties.

Think of a transportation network:

  • Regular graph layers: All roads are treated the same
  • Edge-conditioned layers: Each road has properties (speed limit, distance, traffic)

Examples where edge features matter:

  • Molecules: Bond types (single/double/triple) affect how atoms interact
  • Social networks: Relationship types (friend/colleague/family) influence information flow
  • Knowledge graphs: Relationship types (is-a/part-of/located-in) determine connections
  • Transportation: Road types (highway/street/path) affect travel patterns

This layer learns how to use these edge properties to better aggregate neighbor information.

Constructors

EdgeConditionalConvolutionalLayer(int, int, int, int, IActivationFunction<T>?)

Initializes a new instance of the EdgeConditionalConvolutionalLayer<T> class.

public EdgeConditionalConvolutionalLayer(int inputFeatures, int outputFeatures, int edgeFeatures, int edgeNetworkHiddenDim = 64, IActivationFunction<T>? activationFunction = null)

Parameters

inputFeatures int

Number of input features per node.

outputFeatures int

Number of output features per node.

edgeFeatures int

Number of edge features.

edgeNetworkHiddenDim int

Hidden dimension for edge network (default: 64).

activationFunction IActivationFunction<T>

Activation function to apply.

Remarks

Creates an edge-conditioned convolution layer. The edge network is a 2-layer MLP that transforms edge features into node feature transformation weights.

For Beginners: This creates a new edge-conditioned layer.

Parameters:

  • edgeFeatures: How many properties each connection has
  • edgeNetworkHiddenDim: Size of the network that learns from edge properties (bigger = more expressive but slower)

Example: In a molecule

  • inputFeatures=32: Each atom has 32 properties
  • outputFeatures=64: Transform to 64 properties
  • edgeFeatures=4: Each bond has 4 properties (type, length, strength, angle)

The layer learns to use bond properties to determine how atoms influence each other.

Properties

InputFeatures

Gets the number of input features per node.

public 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.

public 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.

SupportsGpuExecution

Gets whether this layer has a GPU execution implementation for inference.

protected override bool SupportsGpuExecution { get; }

Property Value

bool

Remarks

Override this to return true when the layer implements ForwardGpu(params IGpuTensor<T>[]). The actual CanExecuteOnGpu property combines this with engine availability.

For Beginners: This flag indicates if the layer has GPU code for the forward pass. Set this to true in derived classes that implement ForwardGpu.

SupportsJitCompilation

Gets whether this layer supports JIT compilation.

public override bool SupportsJitCompilation { get; }

Property Value

bool

True if the layer can be JIT compiled, false otherwise.

Remarks

This property indicates whether the layer has implemented ExportComputationGraph() and can benefit from JIT compilation. All layers MUST implement this property.

For Beginners: JIT compilation can make inference 5-10x faster by converting the layer's operations into optimized native code.

Layers should return false if they:

  • Have not yet implemented a working ExportComputationGraph()
  • Use dynamic operations that change based on input data
  • Are too simple to benefit from JIT compilation

When false, the layer will use the standard Forward() method instead.

SupportsTraining

Gets a value indicating whether this layer supports training.

public override bool SupportsTraining { get; }

Property Value

bool

true if the layer has trainable parameters and supports backpropagation; otherwise, false.

Remarks

This property indicates whether the layer can be trained through backpropagation. Layers with trainable parameters such as weights and biases typically return true, while layers that only perform fixed transformations (like pooling or activation layers) typically return false.

For Beginners: This property tells you if the layer can learn from data.

A value of true means:

  • The layer has parameters that can be adjusted during training
  • It will improve its performance as it sees more data
  • It participates in the learning process

A value of false means:

  • The layer doesn't have any adjustable parameters
  • It performs the same operation regardless of training
  • It doesn't need to learn (but may still be useful)

Methods

Backward(Tensor<T>)

Computes the backward pass for this edge-conditional layer.

public override Tensor<T> Backward(Tensor<T> outputGradient)

Parameters

outputGradient Tensor<T>

The gradient from the next layer.

Returns

Tensor<T>

The gradient to propagate to the previous layer.

Remarks

This backward pass computes gradients for all parameters including edge network weights, self-weights, biases, and propagates gradients to the input.

ExportComputationGraph(List<ComputationNode<T>>)

Exports the layer's computation graph for JIT compilation.

public override ComputationNode<T> ExportComputationGraph(List<ComputationNode<T>> inputNodes)

Parameters

inputNodes List<ComputationNode<T>>

List to populate with input computation nodes.

Returns

ComputationNode<T>

The output computation node representing the layer's operation.

Remarks

This method constructs a computation graph representation of the layer's forward pass that can be JIT compiled for faster inference. All layers MUST implement this method to support JIT compilation.

For Beginners: JIT (Just-In-Time) compilation converts the layer's operations into optimized native code for 5-10x faster inference.

To support JIT compilation, a layer must:

  1. Implement this method to export its computation graph
  2. Set SupportsJitCompilation to true
  3. Use ComputationNode and TensorOperations to build the graph

All layers are required to implement this method, even if they set SupportsJitCompilation = false.

Forward(Tensor<T>)

Performs the forward pass of the layer.

public override Tensor<T> Forward(Tensor<T> input)

Parameters

input Tensor<T>

The input tensor to process.

Returns

Tensor<T>

The output tensor after processing.

Remarks

This abstract method must be implemented by derived classes to define the forward pass of the layer. The forward pass transforms the input tensor according to the layer's operation and activation function.

For Beginners: This method processes your data through the layer.

The forward pass:

  • Takes input data from the previous layer or the network input
  • Applies the layer's specific transformation (like convolution or matrix multiplication)
  • Applies any activation function
  • Passes the result to the next layer

This is where the actual data processing happens during both training and prediction.

ForwardGpu(params IGpuTensor<T>[])

Performs the forward pass of the layer on GPU.

public override IGpuTensor<T> ForwardGpu(params IGpuTensor<T>[] inputs)

Parameters

inputs IGpuTensor<T>[]

The GPU-resident input tensor(s).

Returns

IGpuTensor<T>

The GPU-resident output tensor.

Remarks

This method performs the layer's forward computation entirely on GPU. The input and output tensors remain in GPU memory, avoiding expensive CPU-GPU transfers.

For Beginners: This is like Forward() but runs on the graphics card.

The key difference:

  • Forward() uses CPU tensors that may be copied to/from GPU
  • ForwardGpu() keeps everything on GPU the whole time

Override this in derived classes that support GPU acceleration.

Exceptions

NotSupportedException

Thrown when the layer does not support GPU execution.

GetAdjacencyMatrix()

Gets the adjacency matrix currently being used by this layer.

public 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

GetParameters()

Gets all trainable parameters of the layer as a single vector.

public override Vector<T> GetParameters()

Returns

Vector<T>

A vector containing all trainable parameters.

Remarks

This abstract method must be implemented by derived classes to provide access to all trainable parameters of the layer as a single vector. This is useful for optimization algorithms that operate on all parameters at once, or for saving and loading model weights.

For Beginners: This method collects all the learnable values from the layer.

The parameters:

  • Are the numbers that the neural network learns during training
  • Include weights, biases, and other learnable values
  • Are combined into a single long list (vector)

This is useful for:

  • Saving the model to disk
  • Loading parameters from a previously trained model
  • Advanced optimization techniques that need access to all parameters

ResetState()

Resets the internal state of the layer.

public override void ResetState()

Remarks

This abstract method must be implemented by derived classes to reset any internal state the layer maintains between forward and backward passes. This is useful when starting to process a new sequence or when implementing stateful recurrent networks.

For Beginners: This method clears the layer's memory to start fresh.

When resetting the state:

  • Cached inputs and outputs are cleared
  • Any temporary calculations are discarded
  • The layer is ready to process new data without being influenced by previous data

This is important for:

  • Processing a new, unrelated sequence
  • Preventing information from one sequence affecting another
  • Starting a new training episode

SetAdjacencyMatrix(Tensor<T>)

Sets the adjacency matrix that defines the graph structure.

public 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.

SetEdgeFeatures(Tensor<T>)

Sets the edge features for this layer.

public void SetEdgeFeatures(Tensor<T> edgeFeatures)

Parameters

edgeFeatures Tensor<T>

Edge features tensor with shape [batch, numEdges, edgeFeatureDim].

SetParameters(Vector<T>)

Sets the trainable parameters of the layer.

public override void SetParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

A vector containing all parameters to set.

Remarks

This method sets all the trainable parameters of the layer from a single vector of parameters. The parameters vector must have the correct length to match the total number of parameters in the layer. By default, it simply assigns the parameters vector to the Parameters field, but derived classes may override this to handle the parameters differently.

For Beginners: This method updates all the learnable values in the layer.

When setting parameters:

  • The input must be a vector with the correct length
  • The layer parses this vector to set all its internal parameters
  • Throws an error if the input doesn't match the expected number of parameters

This is useful for:

  • Loading a previously saved model
  • Transferring parameters from another model
  • Setting specific parameter values for testing

Exceptions

ArgumentException

Thrown when the parameters vector has incorrect length.

UpdateParameters(T)

Updates the parameters of the layer using the calculated gradients.

public override void UpdateParameters(T learningRate)

Parameters

learningRate T

The learning rate to use for the parameter updates.

Remarks

This abstract method must be implemented by derived classes to define how the layer's parameters are updated during training. The learning rate controls the size of the parameter updates.

For Beginners: This method updates the layer's internal values during training.

When updating parameters:

  • The weights, biases, or other parameters are adjusted to reduce prediction errors
  • The learning rate controls how big each update step is
  • Smaller learning rates mean slower but more stable learning
  • Larger learning rates mean faster but potentially unstable learning

This is how the layer "learns" from data over time, gradually improving its ability to extract useful patterns from inputs.

Edit this page