Class LinkPredictionTask<T>

Namespace

AiDotNet.Data.Structures

Assembly

AiDotNet.dll

Represents a link prediction task where the goal is to predict missing or future edges.

public class LinkPredictionTask<T>

Type Parameters

T

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

Inheritance

object

LinkPredictionTask<T>

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Link prediction aims to predict whether an edge should exist between two nodes based on: - Node features - Graph structure - Edge patterns in the existing graph

For Beginners: Link prediction is like recommending friendships or connections.

Real-world examples:

Social Networks:

• Task: Friend recommendation
• Question: "Will these two users become friends?"
• How: Analyze mutual friends, shared interests, interaction patterns
• Example: "You may know..." suggestions on Facebook/LinkedIn

E-commerce:

• Task: Product recommendation
• Question: "Will this user purchase this product?"
• Graph: Users and products as nodes, purchases as edges
• How: Users with similar purchase history likely buy similar products

Citation Networks:

• Task: Predict future citations
• Question: "Will paper A cite paper B?"
• How: Analyze topic similarity, author connections, citation patterns

Drug Discovery:

• Task: Predict drug-target interactions
• Question: "Will this drug bind to this protein?"
• Graph: Drugs and proteins as nodes, known interactions as edges

Key Techniques:

• Negative sampling: Create non-existent edges as negative examples
• Edge splitting: Hide some edges during training, predict them at test time
• Node pair scoring: Learn to score how likely two nodes should connect

Properties

Graph

The graph data with edges potentially removed for training.

public GraphData<T> Graph { get; set; }

Property Value

GraphData<T>

Remarks

In link prediction, we typically remove a portion of edges from the graph and try to predict them. The graph here contains the training edges only.

IsDirected

Whether the graph is directed (default: false).

public bool IsDirected { get; set; }

Property Value

bool

Remarks

• Directed: Edge from A to B doesn't imply edge from B to A (e.g., Twitter follows)
• Undirected: Edge is bidirectional (e.g., Facebook friendships)

NegativeSamplingRatio

Ratio of negative to positive edges for sampling.

public double NegativeSamplingRatio { get; set; }

Property Value

double

Remarks

Typically 1.0 (balanced) but can be adjusted. Higher ratios make the task harder but can improve model robustness.

TestNegEdges

Negative edge examples for testing. Shape: [num_test_neg_edges, 2].

public Tensor<T> TestNegEdges { get; set; }

Property Value

Tensor<T>

TestPosEdges

Positive edge examples for testing. Shape: [num_test_edges, 2].

public Tensor<T> TestPosEdges { get; set; }

Property Value

Tensor<T>

TrainNegEdges

Negative edge examples (edges that don't exist) for training. Shape: [num_train_neg_edges, 2].

public Tensor<T> TrainNegEdges { get; set; }

Property Value

Tensor<T>

Remarks

These are sampled node pairs that don't have edges. They serve as negative examples to teach the model what connections are unlikely.

For Beginners: Why do we need negative examples?

Imagine teaching someone to recognize friends vs strangers:

• Positive examples: "These people ARE friends" (existing edges)
• Negative examples: "These people are NOT friends" (non-existing edges)

Without negatives, the model might predict everyone is friends with everyone! Negative sampling creates a balanced training set.

TrainPosEdges

Positive edge examples (edges that exist) for training. Shape: [num_train_edges, 2] where each row is [source_node, target_node].

public Tensor<T> TrainPosEdges { get; set; }

Property Value

Tensor<T>

Remarks

These are edges that exist in the original graph and should be predicted as positive.

ValNegEdges

Negative edge examples for validation. Shape: [num_val_neg_edges, 2].

public Tensor<T> ValNegEdges { get; set; }

Property Value

Tensor<T>

ValPosEdges

Positive edge examples for validation. Shape: [num_val_edges, 2].

public Tensor<T> ValPosEdges { get; set; }

Property Value

Tensor<T>