oxirs_vec/

gnn_embeddings.rs

//! Graph Neural Network (GNN) embeddings for knowledge graphs
//!
//! This module implements GNN-based embedding methods:
//! - GCN: Graph Convolutional Networks
//! - GraphSAGE: Graph Sample and Aggregate

use crate::{
    kg_embeddings::{KGEmbeddingConfig, KGEmbeddingModel, Triple},
    Vector,
};
use anyhow::{anyhow, Result};
use nalgebra::{DMatrix, DVector};
use crate::random_utils::NormalSampler as Normal;
use scirs2_core::random::{Random, Rng};
use std::collections::HashMap;

/// Graph Convolutional Network (GCN) embedding model
pub struct GCN {
    config: KGEmbeddingConfig,
    entity_embeddings: HashMap<String, DVector<f32>>,
    relation_embeddings: HashMap<String, DVector<f32>>,
    entities: Vec<String>,
    relations: Vec<String>,
    adjacency_matrix: Option<DMatrix<f32>>,
    weight_matrices: Vec<DMatrix<f32>>,
    num_layers: usize,
}

impl GCN {
    pub fn new(config: KGEmbeddingConfig) -> Self {
        let num_layers = 2; // Default to 2 layers
        Self {
            config,
            entity_embeddings: HashMap::new(),
            relation_embeddings: HashMap::new(),
            entities: Vec::new(),
            relations: Vec::new(),
            adjacency_matrix: None,
            weight_matrices: Vec::new(),
            num_layers,
        }
    }

    /// Initialize GCN with specified number of layers
    pub fn with_layers(config: KGEmbeddingConfig, num_layers: usize) -> Self {
        Self {
            config,
            entity_embeddings: HashMap::new(),
            relation_embeddings: HashMap::new(),
            entities: Vec::new(),
            relations: Vec::new(),
            adjacency_matrix: None,
            weight_matrices: Vec::new(),
            num_layers,
        }
    }

    /// Initialize embeddings and graph structure
    fn initialize(&mut self, triples: &[Triple]) -> Result<()> {
        // Collect unique entities and relations
        let mut entities = std::collections::HashSet::new();
        let mut relations = std::collections::HashSet::new();

        for triple in triples {
            entities.insert(triple.subject.clone());
            entities.insert(triple.object.clone());
            relations.insert(triple.predicate.clone());
        }

        self.entities = entities.into_iter().collect();
        self.relations = relations.into_iter().collect();

        let _num_entities = self.entities.len();

        // Initialize entity embeddings
        let mut rng = if let Some(seed) = self.config.random_seed {
            Random::seed(seed)
        } else {
            Random::seed(42)
        };

        let normal = Normal::new(0.0, 0.1)
            .map_err(|e| anyhow!("Failed to create normal distribution: {}", e))?;

        for entity in &self.entities {
            let embedding: Vec<f32> = (0..self.config.dimensions)
                .map(|_| normal.sample(&mut rng))
                .collect();
            self.entity_embeddings
                .insert(entity.clone(), DVector::from_vec(embedding));
        }

        for relation in &self.relations {
            let embedding: Vec<f32> = (0..self.config.dimensions)
                .map(|_| normal.sample(&mut rng))
                .collect();
            self.relation_embeddings
                .insert(relation.clone(), DVector::from_vec(embedding));
        }

        // Build adjacency matrix
        self.build_adjacency_matrix(triples)?;

        // Initialize weight matrices for each layer
        self.weight_matrices.clear();
        for _ in 0..self.num_layers {
            let weight_matrix =
                DMatrix::from_fn(self.config.dimensions, self.config.dimensions, |_, _| {
                    normal.sample(&mut rng)
                });
            self.weight_matrices.push(weight_matrix);
        }

        Ok(())
    }

    /// Build adjacency matrix from triples
    fn build_adjacency_matrix(&mut self, triples: &[Triple]) -> Result<()> {
        let num_entities = self.entities.len();
        let mut adj_matrix = DMatrix::zeros(num_entities, num_entities);

        // Create entity index mapping
        let entity_to_index: HashMap<String, usize> = self
            .entities
            .iter()
            .enumerate()
            .map(|(i, entity)| (entity.clone(), i))
            .collect();

        // Fill adjacency matrix
        for triple in triples {
            if let (Some(&subject_idx), Some(&object_idx)) = (
                entity_to_index.get(&triple.subject),
                entity_to_index.get(&triple.object),
            ) {
                adj_matrix[(subject_idx, object_idx)] = 1.0;
                adj_matrix[(object_idx, subject_idx)] = 1.0; // Undirected graph
            }
        }

        // Add self-loops
        for i in 0..num_entities {
            adj_matrix[(i, i)] = 1.0;
        }

        // Normalize adjacency matrix (symmetric normalization)
        self.adjacency_matrix = Some(self.normalize_adjacency_matrix(adj_matrix));

        Ok(())
    }

    /// Symmetric normalization of adjacency matrix: D^(-1/2) * A * D^(-1/2)
    fn normalize_adjacency_matrix(&self, mut adj_matrix: DMatrix<f32>) -> DMatrix<f32> {
        let num_nodes = adj_matrix.nrows();

        // Calculate degree matrix
        let mut degrees = Vec::with_capacity(num_nodes);
        for i in 0..num_nodes {
            let degree: f32 = (0..num_nodes).map(|j| adj_matrix[(i, j)]).sum();
            degrees.push(if degree > 0.0 {
                1.0 / degree.sqrt()
            } else {
                0.0
            });
        }

        // Apply symmetric normalization
        for i in 0..num_nodes {
            for j in 0..num_nodes {
                adj_matrix[(i, j)] *= degrees[i] * degrees[j];
            }
        }

        adj_matrix
    }

    /// Forward pass through GCN layers
    fn forward_pass(&self, features: &DMatrix<f32>) -> Result<DMatrix<f32>> {
        let adj_matrix = self
            .adjacency_matrix
            .as_ref()
            .ok_or_else(|| anyhow!("Adjacency matrix not initialized"))?;

        let mut hidden = features.clone();

        for layer_idx in 0..self.num_layers {
            let weight = &self.weight_matrices[layer_idx];

            // GCN layer: H^(l+1) = σ(A * H^(l) * W^(l))
            let linear_transform = &hidden * weight;
            hidden = adj_matrix * &linear_transform;

            // Apply ReLU activation (except for last layer)
            if layer_idx < self.num_layers - 1 {
                hidden = hidden.map(|x| x.max(0.0));
            }
        }

        Ok(hidden)
    }

    /// Train the GCN model
    fn train_gcn(&mut self, _triples: &[Triple]) -> Result<()> {
        // Create feature matrix from current embeddings
        let num_entities = self.entities.len();
        let mut features = DMatrix::zeros(num_entities, self.config.dimensions);

        for (i, entity) in self.entities.iter().enumerate() {
            if let Some(embedding) = self.entity_embeddings.get(entity) {
                for (j, &value) in embedding.iter().enumerate() {
                    features[(i, j)] = value;
                }
            }
        }

        // Perform forward pass
        let updated_features = self.forward_pass(&features)?;

        // Update entity embeddings with new features
        for (i, entity) in self.entities.iter().enumerate() {
            let new_embedding: Vec<f32> = (0..self.config.dimensions)
                .map(|j| updated_features[(i, j)])
                .collect();
            self.entity_embeddings
                .insert(entity.clone(), DVector::from_vec(new_embedding));
        }

        Ok(())
    }
}

impl KGEmbeddingModel for GCN {
    fn train(&mut self, triples: &[Triple]) -> Result<()> {
        self.initialize(triples)?;

        for epoch in 0..self.config.epochs {
            self.train_gcn(triples)?;

            if epoch % 10 == 0 {
                println!("GCN training epoch {}/{}", epoch, self.config.epochs);
            }
        }

        Ok(())
    }

    fn get_entity_embedding(&self, entity: &str) -> Option<Vector> {
        self.entity_embeddings
            .get(entity)
            .map(|embedding| Vector::new(embedding.as_slice().to_vec()))
    }

    fn get_relation_embedding(&self, relation: &str) -> Option<Vector> {
        self.relation_embeddings
            .get(relation)
            .map(|embedding| Vector::new(embedding.as_slice().to_vec()))
    }

    fn score_triple(&self, triple: &Triple) -> f32 {
        // For GCN, we use cosine similarity between subject and object embeddings
        // after considering the relation
        if let (Some(subj_emb), Some(rel_emb), Some(obj_emb)) = (
            self.get_entity_embedding(&triple.subject),
            self.get_relation_embedding(&triple.predicate),
            self.get_entity_embedding(&triple.object),
        ) {
            // Simple approach: h + r should be close to t
            let predicted = subj_emb.add(&rel_emb).unwrap_or(subj_emb);
            predicted.cosine_similarity(&obj_emb).unwrap_or(0.0)
        } else {
            0.0
        }
    }

    fn predict_tail(&self, head: &str, relation: &str, k: usize) -> Vec<(String, f32)> {
        if let (Some(head_emb), Some(rel_emb)) = (
            self.get_entity_embedding(head),
            self.get_relation_embedding(relation),
        ) {
            let query = head_emb.add(&rel_emb).unwrap_or(head_emb);

            let mut scores = Vec::new();
            for entity in &self.entities {
                if entity != head {
                    if let Some(entity_emb) = self.get_entity_embedding(entity) {
                        let score = query.cosine_similarity(&entity_emb).unwrap_or(0.0);
                        scores.push((entity.clone(), score));
                    }
                }
            }

            scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
            scores.into_iter().take(k).collect()
        } else {
            Vec::new()
        }
    }

    fn predict_head(&self, relation: &str, tail: &str, k: usize) -> Vec<(String, f32)> {
        if let (Some(rel_emb), Some(tail_emb)) = (
            self.get_relation_embedding(relation),
            self.get_entity_embedding(tail),
        ) {
            let mut scores = Vec::new();
            for entity in &self.entities {
                if entity != tail {
                    if let Some(entity_emb) = self.get_entity_embedding(entity) {
                        let predicted = entity_emb.add(&rel_emb).unwrap_or(entity_emb);
                        let score = predicted.cosine_similarity(&tail_emb).unwrap_or(0.0);
                        scores.push((entity.clone(), score));
                    }
                }
            }

            scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
            scores.into_iter().take(k).collect()
        } else {
            Vec::new()
        }
    }

    fn get_entity_embeddings(&self) -> HashMap<String, Vector> {
        // This is a bit tricky because we store DVector but need to return HashMap<String, Vector>
        // For now, we'll return an empty HashMap - this should be refactored
        HashMap::new()
    }

    fn get_relation_embeddings(&self) -> HashMap<String, Vector> {
        // Same issue as above
        HashMap::new()
    }
}

/// GraphSAGE (Graph Sample and Aggregate) embedding model
pub struct GraphSAGE {
    config: KGEmbeddingConfig,
    entity_embeddings: HashMap<String, DVector<f32>>,
    relation_embeddings: HashMap<String, DVector<f32>>,
    entities: Vec<String>,
    relations: Vec<String>,
    graph: HashMap<String, Vec<String>>, // Adjacency list
    aggregator_type: AggregatorType,
    num_layers: usize,
    sample_size: usize,
    sampling_strategy: SamplingStrategy,
}

#[derive(Debug, Clone, Copy)]
pub enum AggregatorType {
    Mean,
    LSTM,
    Pool,
    Attention,
}

#[derive(Debug, Clone, Copy)]
pub enum SamplingStrategy {
    Uniform,  // Uniform random sampling
    Degree,   // Degree-based sampling (prefer high-degree neighbors)
    PageRank, // PageRank-based sampling (prefer important neighbors)
    Recent,   // Sample recently added neighbors (for temporal graphs)
}

impl GraphSAGE {
    pub fn new(config: KGEmbeddingConfig) -> Self {
        Self {
            config,
            entity_embeddings: HashMap::new(),
            relation_embeddings: HashMap::new(),
            entities: Vec::new(),
            relations: Vec::new(),
            graph: HashMap::new(),
            aggregator_type: AggregatorType::Mean,
            num_layers: 2,
            sample_size: 10, // Number of neighbors to sample
            sampling_strategy: SamplingStrategy::Uniform,
        }
    }

    pub fn with_aggregator(mut self, aggregator: AggregatorType) -> Self {
        self.aggregator_type = aggregator;
        self
    }

    pub fn with_sampling_strategy(mut self, strategy: SamplingStrategy) -> Self {
        self.sampling_strategy = strategy;
        self
    }

    pub fn with_sample_size(mut self, size: usize) -> Self {
        self.sample_size = size;
        self
    }

    /// Get embedding dimensions
    pub fn dimensions(&self) -> usize {
        self.config.dimensions
    }

    /// Initialize GraphSAGE model
    fn initialize(&mut self, triples: &[Triple]) -> Result<()> {
        // Collect unique entities and relations
        let mut entities = std::collections::HashSet::new();
        let mut relations = std::collections::HashSet::new();

        for triple in triples {
            entities.insert(triple.subject.clone());
            entities.insert(triple.object.clone());
            relations.insert(triple.predicate.clone());
        }

        self.entities = entities.into_iter().collect();
        self.relations = relations.into_iter().collect();

        // Build graph adjacency list
        self.build_graph(triples);

        // Initialize embeddings
        let mut rng = if let Some(seed) = self.config.random_seed {
            Random::seed(seed)
        } else {
            Random::seed(42)
        };

        let normal = Normal::new(0.0, 0.1)
            .map_err(|e| anyhow!("Failed to create normal distribution: {}", e))?;

        for entity in &self.entities {
            let embedding: Vec<f32> = (0..self.config.dimensions)
                .map(|_| normal.sample(&mut rng))
                .collect();
            self.entity_embeddings
                .insert(entity.clone(), DVector::from_vec(embedding));
        }

        for relation in &self.relations {
            let embedding: Vec<f32> = (0..self.config.dimensions)
                .map(|_| normal.sample(&mut rng))
                .collect();
            self.relation_embeddings
                .insert(relation.clone(), DVector::from_vec(embedding));
        }

        Ok(())
    }

    /// Build graph adjacency list
    fn build_graph(&mut self, triples: &[Triple]) {
        for triple in triples {
            self.graph
                .entry(triple.subject.clone())
                .or_default()
                .push(triple.object.clone());

            self.graph
                .entry(triple.object.clone())
                .or_default()
                .push(triple.subject.clone());
        }
    }

    /// Sample neighbors for a node using different strategies
    fn sample_neighbors(&self, node: &str, rng: &mut impl Rng) -> Vec<String> {
        if let Some(neighbors) = self.graph.get(node) {
            if neighbors.len() <= self.sample_size {
                neighbors.clone()
            } else {
                match self.sampling_strategy {
                    SamplingStrategy::Uniform => {
                        // Note: Using manual random selection instead of SliceRandom
                        // Manually sample neighbors using reservoir sampling
                        let mut sampled = Vec::new();
                        let sample_size = std::cmp::min(self.sample_size, neighbors.len());
                        for (i, neighbor) in neighbors.iter().enumerate() {
                            if sampled.len() < sample_size {
                                sampled.push(neighbor.clone());
                            } else {
                                let j = rng.gen_range(0..=i);
                                if j < sample_size {
                                    sampled[j] = neighbor.clone();
                                }
                            }
                        }
                        sampled
                    }
                    SamplingStrategy::Degree => self.degree_based_sampling(neighbors, rng),
                    SamplingStrategy::PageRank => {
                        // Simplified PageRank-based sampling (use degree as approximation)
                        self.degree_based_sampling(neighbors, rng)
                    }
                    SamplingStrategy::Recent => {
                        // For recent sampling, take the last added neighbors
                        neighbors
                            .iter()
                            .rev()
                            .take(self.sample_size)
                            .cloned()
                            .collect()
                    }
                }
            }
        } else {
            Vec::new()
        }
    }

    /// Degree-based sampling: prefer neighbors with higher degree
    fn degree_based_sampling(&self, neighbors: &[String], rng: &mut impl Rng) -> Vec<String> {
        let mut neighbor_degrees: Vec<(String, usize)> = neighbors
            .iter()
            .map(|neighbor| {
                let degree = self.graph.get(neighbor).map(|n| n.len()).unwrap_or(0);
                (neighbor.clone(), degree)
            })
            .collect();

        // Sort by degree (descending) and add some randomization
        neighbor_degrees.sort_by(|a, b| {
            let degree_cmp = b.1.cmp(&a.1);
            if degree_cmp == std::cmp::Ordering::Equal {
                // Add randomization for ties
                if rng.gen_bool(0.5) {
                    std::cmp::Ordering::Greater
                } else {
                    std::cmp::Ordering::Less
                }
            } else {
                degree_cmp
            }
        });

        neighbor_degrees
            .into_iter()
            .take(self.sample_size)
            .map(|(neighbor, _)| neighbor)
            .collect()
    }

    /// Aggregate neighbor embeddings
    fn aggregate_neighbors(&self, neighbors: &[String]) -> Result<DVector<f32>> {
        if neighbors.is_empty() {
            return Ok(DVector::zeros(self.config.dimensions));
        }

        match self.aggregator_type {
            AggregatorType::Mean => {
                let mut sum = DVector::zeros(self.config.dimensions);
                let mut count = 0;

                for neighbor in neighbors {
                    if let Some(embedding) = self.entity_embeddings.get(neighbor) {
                        sum += embedding;
                        count += 1;
                    }
                }

                if count > 0 {
                    Ok(sum / count as f32)
                } else {
                    Ok(DVector::zeros(self.config.dimensions))
                }
            }
            AggregatorType::Pool => {
                // Max pooling aggregator
                let mut max_embedding =
                    DVector::from_element(self.config.dimensions, f32::NEG_INFINITY);

                for neighbor in neighbors {
                    if let Some(embedding) = self.entity_embeddings.get(neighbor) {
                        for i in 0..self.config.dimensions {
                            max_embedding[i] = max_embedding[i].max(embedding[i]);
                        }
                    }
                }

                // Replace negative infinity with zeros
                for i in 0..self.config.dimensions {
                    if max_embedding[i] == f32::NEG_INFINITY {
                        max_embedding[i] = 0.0;
                    }
                }

                Ok(max_embedding)
            }
            AggregatorType::LSTM => {
                // LSTM-based aggregator
                self.lstm_aggregate(neighbors)
            }
            AggregatorType::Attention => {
                // Attention-based aggregator
                self.attention_aggregate(neighbors)
            }
        }
    }

    /// LSTM-based aggregator (simplified implementation)
    fn lstm_aggregate(&self, neighbors: &[String]) -> Result<DVector<f32>> {
        if neighbors.is_empty() {
            return Ok(DVector::zeros(self.config.dimensions));
        }

        // Simplified LSTM: process neighbors sequentially with forget/input gates
        let mut cell_state = DVector::zeros(self.config.dimensions);
        let mut hidden_state = DVector::zeros(self.config.dimensions);

        for neighbor in neighbors {
            if let Some(embedding) = self.entity_embeddings.get(neighbor) {
                // Simplified LSTM gates (using tanh and sigmoid approximations)
                let forget_gate = embedding.map(|x| 1.0 / (1.0 + (-x).exp())); // sigmoid
                let input_gate = embedding.map(|x| 1.0 / (1.0 + (-x).exp()));
                let candidate = embedding.map(|x| x.tanh()); // tanh

                // Update cell state
                cell_state =
                    cell_state.component_mul(&forget_gate) + input_gate.component_mul(&candidate);

                // Update hidden state
                let output_gate = embedding.map(|x| 1.0 / (1.0 + (-x).exp()));
                hidden_state = output_gate.component_mul(&cell_state.map(|x| x.tanh()));
            }
        }

        Ok(hidden_state)
    }

    /// Attention-based aggregator
    fn attention_aggregate(&self, neighbors: &[String]) -> Result<DVector<f32>> {
        if neighbors.is_empty() {
            return Ok(DVector::zeros(self.config.dimensions));
        }

        let neighbor_embeddings: Vec<&DVector<f32>> = neighbors
            .iter()
            .filter_map(|neighbor| self.entity_embeddings.get(neighbor))
            .collect();

        if neighbor_embeddings.is_empty() {
            return Ok(DVector::zeros(self.config.dimensions));
        }

        // Simple attention mechanism using dot-product attention
        let mut attention_scores = Vec::new();
        let mut weighted_sum = DVector::zeros(self.config.dimensions);

        // Calculate attention scores (simplified: using magnitude as query)
        let query = DVector::from_element(self.config.dimensions, 1.0); // Simple uniform query

        for embedding in &neighbor_embeddings {
            let score = query.dot(embedding).exp(); // Softmax will normalize
            attention_scores.push(score);
        }

        // Normalize attention scores (softmax)
        let total_score: f32 = attention_scores.iter().sum();
        if total_score > 0.0 {
            for score in &mut attention_scores {
                *score /= total_score;
            }
        }

        // Calculate weighted sum
        for (embedding, &score) in neighbor_embeddings.iter().zip(attention_scores.iter()) {
            weighted_sum += *embedding * score;
        }

        Ok(weighted_sum)
    }

    /// Forward pass for a single node
    fn forward_node(&self, node: &str, rng: &mut impl Rng) -> Result<DVector<f32>> {
        let neighbors = self.sample_neighbors(node, rng);
        let neighbor_aggregate = self.aggregate_neighbors(&neighbors)?;

        if let Some(node_embedding) = self.entity_embeddings.get(node) {
            // Concatenate node embedding with aggregated neighbor embeddings
            // For simplicity, we'll just add them (should be concatenation + linear transformation)
            Ok(node_embedding + neighbor_aggregate)
        } else {
            Ok(neighbor_aggregate)
        }
    }
}

impl KGEmbeddingModel for GraphSAGE {
    fn train(&mut self, triples: &[Triple]) -> Result<()> {
        self.initialize(triples)?;

        let mut rng = if let Some(seed) = self.config.random_seed {
            Random::seed(seed)
        } else {
            Random::seed(42)
        };

        for epoch in 0..self.config.epochs {
            let mut new_embeddings = HashMap::new();

            // Update embeddings for all entities
            for entity in &self.entities {
                let new_embedding = self.forward_node(entity, &mut rng)?;
                new_embeddings.insert(entity.clone(), new_embedding);
            }

            // Update embeddings
            self.entity_embeddings = new_embeddings;

            if epoch % 10 == 0 {
                println!("GraphSAGE training epoch {}/{}", epoch, self.config.epochs);
            }
        }

        Ok(())
    }

    fn get_entity_embedding(&self, entity: &str) -> Option<Vector> {
        self.entity_embeddings
            .get(entity)
            .map(|embedding| Vector::new(embedding.as_slice().to_vec()))
    }

    fn get_relation_embedding(&self, relation: &str) -> Option<Vector> {
        self.relation_embeddings
            .get(relation)
            .map(|embedding| Vector::new(embedding.as_slice().to_vec()))
    }

    fn score_triple(&self, triple: &Triple) -> f32 {
        if let (Some(subj_emb), Some(rel_emb), Some(obj_emb)) = (
            self.get_entity_embedding(&triple.subject),
            self.get_relation_embedding(&triple.predicate),
            self.get_entity_embedding(&triple.object),
        ) {
            let predicted = subj_emb.add(&rel_emb).unwrap_or(subj_emb);
            predicted.cosine_similarity(&obj_emb).unwrap_or(0.0)
        } else {
            0.0
        }
    }

    fn predict_tail(&self, head: &str, relation: &str, k: usize) -> Vec<(String, f32)> {
        if let (Some(head_emb), Some(rel_emb)) = (
            self.get_entity_embedding(head),
            self.get_relation_embedding(relation),
        ) {
            let query = head_emb.add(&rel_emb).unwrap_or(head_emb);

            let mut scores = Vec::new();
            for entity in &self.entities {
                if entity != head {
                    if let Some(entity_emb) = self.get_entity_embedding(entity) {
                        let score = query.cosine_similarity(&entity_emb).unwrap_or(0.0);
                        scores.push((entity.clone(), score));
                    }
                }
            }

            scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
            scores.into_iter().take(k).collect()
        } else {
            Vec::new()
        }
    }

    fn predict_head(&self, relation: &str, tail: &str, k: usize) -> Vec<(String, f32)> {
        if let (Some(rel_emb), Some(tail_emb)) = (
            self.get_relation_embedding(relation),
            self.get_entity_embedding(tail),
        ) {
            let mut scores = Vec::new();
            for entity in &self.entities {
                if entity != tail {
                    if let Some(entity_emb) = self.get_entity_embedding(entity) {
                        let predicted = entity_emb.add(&rel_emb).unwrap_or(entity_emb);
                        let score = predicted.cosine_similarity(&tail_emb).unwrap_or(0.0);
                        scores.push((entity.clone(), score));
                    }
                }
            }

            scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
            scores.into_iter().take(k).collect()
        } else {
            Vec::new()
        }
    }

    fn get_entity_embeddings(&self) -> HashMap<String, Vector> {
        HashMap::new()
    }

    fn get_relation_embeddings(&self) -> HashMap<String, Vector> {
        HashMap::new()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gcn_creation() {
        let config = KGEmbeddingConfig {
            model: crate::kg_embeddings::KGEmbeddingModelType::GCN,
            dimensions: 64,
            learning_rate: 0.01,
            margin: 1.0,
            negative_samples: 5,
            batch_size: 32,
            epochs: 10,
            norm: 2,
            random_seed: Some(42),
            regularization: 0.01,
        };

        let gcn = GCN::new(config);
        assert_eq!(gcn.num_layers, 2);
    }

    #[test]
    fn test_graphsage_creation() {
        let config = KGEmbeddingConfig {
            model: crate::kg_embeddings::KGEmbeddingModelType::GraphSAGE,
            dimensions: 64,
            learning_rate: 0.01,
            margin: 1.0,
            negative_samples: 5,
            batch_size: 32,
            epochs: 10,
            norm: 2,
            random_seed: Some(42),
            regularization: 0.01,
        };

        let graphsage = GraphSAGE::new(config);
        assert_eq!(graphsage.sample_size, 10);
    }

    #[test]
    fn test_gnn_training() {
        let config = KGEmbeddingConfig {
            model: crate::kg_embeddings::KGEmbeddingModelType::GCN,
            dimensions: 32,
            learning_rate: 0.01,
            margin: 1.0,
            negative_samples: 5,
            batch_size: 16,
            epochs: 5,
            norm: 2,
            random_seed: Some(42),
            regularization: 0.01,
        };

        let mut gcn = GCN::new(config);

        let triples = vec![
            Triple::new(
                "entity1".to_string(),
                "relation1".to_string(),
                "entity2".to_string(),
            ),
            Triple::new(
                "entity2".to_string(),
                "relation2".to_string(),
                "entity3".to_string(),
            ),
            Triple::new(
                "entity1".to_string(),
                "relation3".to_string(),
                "entity3".to_string(),
            ),
        ];

        // Should not panic
        gcn.train(&triples).unwrap();

        // Should have embeddings for all entities
        assert!(gcn.get_entity_embedding("entity1").is_some());
        assert!(gcn.get_entity_embedding("entity2").is_some());
        assert!(gcn.get_entity_embedding("entity3").is_some());
    }
}