Table of Contents

Class LinkPredictionModel<T>

Namespace
AiDotNet.NeuralNetworks.Tasks.Graph
Assembly
AiDotNet.dll

Implements a complete neural network model for link prediction tasks on graphs.

public class LinkPredictionModel<T> : NeuralNetworkBase<T>, INeuralNetworkModel<T>, INeuralNetwork<T>, IFullModel<T, Tensor<T>, Tensor<T>>, IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Tensor<T>, Tensor<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>, IGradientComputable<T, Tensor<T>, Tensor<T>>, IJitCompilable<T>, IInterpretableModel<T>, IInputGradientComputable<T>, IDisposable

Type Parameters

T

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

Inheritance
LinkPredictionModel<T>
Implements
IFullModel<T, Tensor<T>, Tensor<T>>
IModel<Tensor<T>, Tensor<T>, ModelMetadata<T>>
IParameterizable<T, Tensor<T>, Tensor<T>>
ICloneable<IFullModel<T, Tensor<T>, Tensor<T>>>
IGradientComputable<T, Tensor<T>, Tensor<T>>
Inherited Members
Extension Methods

Remarks

Link prediction predicts whether edges should exist between node pairs using: - Node features - Graph structure - Learned node embeddings

For Beginners: This model predicts connections between nodes.

How it works:

  1. Encode: Learn embeddings for all nodes using GNN layers

    Input: Node features + Graph structure
Process: Stack of graph conv layers
Output: Node embeddings [num_nodes, embedding_dim]

  2. Decode: Score node pairs to predict edges

    Input: Node pair (i, j)
Compute: score = f(embedding[i], embedding[j])
Common functions:
- Dot product: z_i * z_j
- Concatenation + MLP: MLP([z_i || z_j])
- Distance-based: -||z_i - z_j||^2

  3. Train: Learn to score existing edges high, non-existing edges low

Example:

Friend recommendation:
- Encode users as embeddings using friend network
- For user pair (Alice, Bob):
  * Compute score from their embeddings
  * High score -> Likely to be friends
  * Low score -> Unlikely to be friends

Constructors

LinkPredictionModel(NeuralNetworkArchitecture<T>, int, int, int, double, LinkPredictionDecoder, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>?, ILossFunction<T>?, double)

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

public LinkPredictionModel(NeuralNetworkArchitecture<T> architecture, int hiddenDim = 64, int embeddingDim = 32, int numLayers = 2, double dropoutRate = 0.5, LinkPredictionModel<T>.LinkPredictionDecoder decoderType = LinkPredictionDecoder.DotProduct, IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>? optimizer = null, ILossFunction<T>? lossFunction = null, double maxGradNorm = 1)

Parameters

architecture NeuralNetworkArchitecture<T>

The neural network architecture defining input/output sizes and layers.

hiddenDim int

Hidden dimension for intermediate layers (default: 64).

embeddingDim int

Dimension of node embeddings (default: 32).

numLayers int

Number of graph convolutional layers (default: 2).

dropoutRate double

Dropout rate for regularization (default: 0.5).

decoderType LinkPredictionModel<T>.LinkPredictionDecoder

Method for combining node embeddings into edge scores.

optimizer IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>

Optional optimizer for training.

lossFunction ILossFunction<T>

Optional loss function for training.

maxGradNorm double

Maximum gradient norm for clipping (default: 1.0).

Remarks

For Beginners: Creating a link prediction model: 

// Create architecture for friend prediction
var architecture = new NeuralNetworkArchitecture<double>(
    InputType.OneDimensional,
    NeuralNetworkTaskType.BinaryClassification,
    NetworkComplexity.Simple,
    inputSize: 128,    // User features
    outputSize: 1);    // Edge score

// Create model with default layers
var model = new LinkPredictionModel<double>(architecture);

// Train on link prediction task
var history = model.TrainOnTask(task, epochs: 100, learningRate: 0.01);

Properties

DropoutRate

Gets the dropout rate for regularization.

public double DropoutRate { get; }

Property Value

double

EmbeddingDim

Gets the embedding dimension.

public int EmbeddingDim { get; }

Property Value

int

HiddenDim

Gets the hidden dimension size.

public int HiddenDim { get; }

Property Value

int

InputFeatures

Gets the number of input features per node.

public int InputFeatures { get; }

Property Value

int

NumLayers

Gets the number of GNN layers.

public int NumLayers { get; }

Property Value

int

Methods

Backward(Tensor<T>)

Performs a backward pass through the network.

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

Parameters

outputGradient Tensor<T>

Gradient of loss with respect to output.

Returns

Tensor<T>

Gradient with respect to input.

CreateNewInstance()

Creates a new instance of this network type for cloning or deserialization.

protected override IFullModel<T, Tensor<T>, Tensor<T>> CreateNewInstance()

Returns

IFullModel<T, Tensor<T>, Tensor<T>>

DeserializeNetworkSpecificData(BinaryReader)

Deserializes network-specific data from a binary reader.

protected override void DeserializeNetworkSpecificData(BinaryReader reader)

Parameters

reader BinaryReader

EvaluateOnTask(LinkPredictionTask<T>)

Evaluates the model on test edges.

public double EvaluateOnTask(LinkPredictionTask<T> task)

Parameters

task LinkPredictionTask<T>

Returns

double

Forward(Tensor<T>)

Performs a forward pass through the encoder network.

public Tensor<T> Forward(Tensor<T> nodeFeatures)

Parameters

nodeFeatures Tensor<T>

Node feature tensor [num_nodes, input_features].

Returns

Tensor<T>

Node embeddings [num_nodes, embedding_dim].

GetModelMetadata()

Gets metadata about this model for serialization and identification.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

GetParameters()

Gets all parameters as a vector.

public override Vector<T> GetParameters()

Returns

Vector<T>

InitializeLayers()

Initializes the layers of the neural network based on the provided architecture.

protected override void InitializeLayers()

Predict(Tensor<T>)

Makes a prediction using the trained network.

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

Parameters

input Tensor<T>

The input tensor containing node features.

Returns

Tensor<T>

The node embeddings tensor.

PredictEdges(Tensor<T>)

Computes edge scores for given node pairs.

public Tensor<T> PredictEdges(Tensor<T> edges)

Parameters

edges Tensor<T>

Edge tensor of shape [num_edges, 2] where each row is [source, target].

Returns

Tensor<T>

Edge scores of shape [num_edges].

SerializeNetworkSpecificData(BinaryWriter)

Serializes network-specific data to a binary writer.

protected override void SerializeNetworkSpecificData(BinaryWriter writer)

Parameters

writer BinaryWriter

SetAdjacencyMatrix(Tensor<T>)

Sets the adjacency matrix for all graph layers in the model.

public void SetAdjacencyMatrix(Tensor<T> adjacencyMatrix)

Parameters

adjacencyMatrix Tensor<T>

The graph adjacency matrix.

Train(Tensor<T>, Tensor<T>)

Trains the network on a single batch of data.

public override void Train(Tensor<T> input, Tensor<T> expectedOutput)

Parameters

input Tensor<T>

The input node features.

expectedOutput Tensor<T>

The expected output (edge scores).

TrainOnTask(LinkPredictionTask<T>, int, double)

Trains the model on a link prediction task.

public Dictionary<string, List<double>> TrainOnTask(LinkPredictionTask<T> task, int epochs, double learningRate = 0.01)

Parameters

task LinkPredictionTask<T>

The link prediction task with graph data and edge splits.

epochs int

Number of training epochs.

learningRate double

Learning rate for optimization.

Returns

Dictionary<string, List<double>>

Training history with loss and metrics per epoch.

UpdateParameters(Vector<T>)

Updates the parameters of all layers in the network.

public override void UpdateParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

A vector containing all parameters for the network.

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