oxirs-embed 0.2.4

//! Research Publication Networks - Academic Knowledge Graph Embeddings
//!
//! This module provides specialized embeddings and analysis for research publication networks,
//! including author embeddings, citation analysis, collaboration networks, and impact prediction.

use crate::Vector;
use anyhow::Result;
use chrono::{DateTime, Utc};
use scirs2_core::random::{Random, RngExt};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::sync::{Arc, RwLock};
use tokio::task::JoinHandle;
use tracing::{debug, info};

/// Research publication network analyzer and embedding generator
pub struct ResearchNetworkAnalyzer {
    /// Author embeddings cache
    author_embeddings: Arc<RwLock<HashMap<String, AuthorEmbedding>>>,
    /// Publication embeddings cache
    publication_embeddings: Arc<RwLock<HashMap<String, PublicationEmbedding>>>,
    /// Citation network graph
    citation_network: Arc<RwLock<CitationNetwork>>,
    /// Collaboration network
    collaboration_network: Arc<RwLock<CollaborationNetwork>>,
    /// Topic models
    topic_models: Arc<RwLock<HashMap<String, TopicModel>>>,
    /// Configuration
    config: ResearchNetworkConfig,
    /// Background analysis tasks
    analysis_tasks: Vec<JoinHandle<()>>,
}

/// Configuration for research network analysis
#[derive(Debug, Clone)]
pub struct ResearchNetworkConfig {
    /// Maximum number of authors to track
    pub max_authors: usize,
    /// Maximum number of publications to track
    pub max_publications: usize,
    /// Citation network update interval (hours)
    pub citation_update_interval_hours: u64,
    /// Collaboration analysis interval (hours)
    pub collaboration_analysis_interval_hours: u64,
    /// Impact prediction model refresh interval (hours)
    pub impact_prediction_refresh_hours: u64,
    /// Enable real-time citation tracking
    pub enable_real_time_citation_tracking: bool,
    /// Minimum citation count for impact analysis
    pub min_citation_threshold: u32,
    /// Topic modeling configuration
    pub topic_config: TopicModelingConfig,
    /// Embedding dimension
    pub embedding_dimension: usize,
}

impl Default for ResearchNetworkConfig {
    fn default() -> Self {
        Self {
            max_authors: 100_000,
            max_publications: 1_000_000,
            citation_update_interval_hours: 24,
            collaboration_analysis_interval_hours: 12,
            impact_prediction_refresh_hours: 48,
            enable_real_time_citation_tracking: true,
            min_citation_threshold: 5,
            topic_config: TopicModelingConfig::default(),
            embedding_dimension: 512,
        }
    }
}

/// Topic modeling configuration
#[derive(Debug, Clone)]
pub struct TopicModelingConfig {
    /// Number of topics to extract
    pub num_topics: usize,
    /// Minimum word frequency
    pub min_word_freq: u32,
    /// Maximum document frequency ratio
    pub max_doc_freq_ratio: f64,
    /// LDA iterations
    pub lda_iterations: u32,
    /// Topic coherence threshold
    pub coherence_threshold: f64,
}

impl Default for TopicModelingConfig {
    fn default() -> Self {
        Self {
            num_topics: 50,
            min_word_freq: 5,
            max_doc_freq_ratio: 0.8,
            lda_iterations: 1000,
            coherence_threshold: 0.4,
        }
    }
}

/// Author information and embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AuthorEmbedding {
    /// Author unique identifier
    pub author_id: String,
    /// Author name
    pub name: String,
    /// Author affiliations
    pub affiliations: Vec<String>,
    /// Research interests/topics
    pub research_topics: Vec<String>,
    /// H-index
    pub h_index: f64,
    /// Total citation count
    pub citation_count: u64,
    /// Publication count
    pub publication_count: u64,
    /// Author embedding vector
    pub embedding: Vector,
    /// Collaboration score
    pub collaboration_score: f64,
    /// Impact score
    pub impact_score: f64,
    /// Career stage
    pub career_stage: CareerStage,
    /// Last updated
    pub last_updated: DateTime<Utc>,
}

/// Publication information and embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PublicationEmbedding {
    /// Publication unique identifier
    pub publication_id: String,
    /// Title
    pub title: String,
    /// Abstract
    pub abstract_text: String,
    /// Authors
    pub authors: Vec<String>,
    /// Venue (journal/conference)
    pub venue: String,
    /// Publication year
    pub year: u32,
    /// Citation count
    pub citation_count: u64,
    /// Topic distribution
    pub topic_distribution: Vec<f64>,
    /// Publication embedding vector
    pub embedding: Vector,
    /// Impact prediction score
    pub predicted_impact: f64,
    /// Publication type
    pub publication_type: PublicationType,
    /// DOI or other identifier
    pub doi: Option<String>,
    /// Last updated
    pub last_updated: DateTime<Utc>,
}

/// Career stage classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CareerStage {
    EarlyCareer,
    MidCareer,
    SeniorCareer,
    Emeritus,
    Unknown,
}

/// Publication type classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PublicationType {
    JournalArticle,
    ConferencePaper,
    BookChapter,
    Book,
    Preprint,
    Thesis,
    TechnicalReport,
    Other,
}

/// Citation network representation
#[derive(Debug, Clone)]
pub struct CitationNetwork {
    /// Citation edges: (citing_paper, cited_paper, citation_context)
    pub citations: HashMap<String, Vec<Citation>>,
    /// Co-citation relationships
    pub co_citations: HashMap<String, Vec<CoCitation>>,
    /// Bibliographic coupling
    pub bibliographic_coupling: HashMap<String, Vec<BibliographicCoupling>>,
    /// Citation patterns over time
    pub temporal_patterns: HashMap<String, Vec<TemporalCitation>>,
}

/// Citation information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Citation {
    /// Citing paper ID
    pub citing_paper: String,
    /// Cited paper ID
    pub cited_paper: String,
    /// Citation context/sentence
    pub context: String,
    /// Citation type (supportive, contrasting, neutral)
    pub citation_type: CitationType,
    /// Position in the paper (intro, methods, results, discussion)
    pub section: PaperSection,
    /// Timestamp of citation
    pub timestamp: DateTime<Utc>,
}

/// Citation type classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CitationType {
    Supportive,
    Contrasting,
    Neutral,
    Background,
    Methodological,
}

/// Paper section where citation occurs
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PaperSection {
    Introduction,
    RelatedWork,
    Methods,
    Results,
    Discussion,
    Conclusion,
    Other,
}

/// Co-citation relationship
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoCitation {
    /// First paper
    pub paper1: String,
    /// Second paper
    pub paper2: String,
    /// Number of papers citing both
    pub co_citation_count: u32,
    /// Similarity score
    pub similarity_score: f64,
}

/// Bibliographic coupling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BibliographicCoupling {
    /// First paper
    pub paper1: String,
    /// Second paper
    pub paper2: String,
    /// Number of shared references
    pub shared_references: u32,
    /// Coupling strength
    pub coupling_strength: f64,
}

/// Temporal citation pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalCitation {
    /// Paper ID
    pub paper_id: String,
    /// Citation timestamp
    pub timestamp: DateTime<Utc>,
    /// Citations at this time
    pub citation_count: u64,
    /// Velocity (citations per time unit)
    pub citation_velocity: f64,
}

/// Collaboration network
#[derive(Debug, Clone)]
pub struct CollaborationNetwork {
    /// Author collaborations: (author1, author2, collaboration_strength)
    pub collaborations: HashMap<String, Vec<Collaboration>>,
    /// Research groups/communities
    pub research_communities: Vec<ResearchCommunity>,
    /// Collaboration patterns over time
    pub temporal_collaborations: HashMap<String, Vec<TemporalCollaboration>>,
}

/// Collaboration between authors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Collaboration {
    /// First author
    pub author1: String,
    /// Second author
    pub author2: String,
    /// Number of joint publications
    pub joint_publications: u32,
    /// Collaboration strength score
    pub strength: f64,
    /// Shared research topics
    pub shared_topics: Vec<String>,
    /// First collaboration date
    pub first_collaboration: DateTime<Utc>,
    /// Last collaboration date
    pub last_collaboration: DateTime<Utc>,
}

/// Research community/cluster
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResearchCommunity {
    /// Community ID
    pub community_id: String,
    /// Community members (author IDs)
    pub members: Vec<String>,
    /// Community topics
    pub topics: Vec<String>,
    /// Central/influential members
    pub central_members: Vec<String>,
    /// Community coherence score
    pub coherence_score: f64,
    /// Community size
    pub size: usize,
}

/// Temporal collaboration pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalCollaboration {
    /// Author ID
    pub author_id: String,
    /// Time period
    pub timestamp: DateTime<Utc>,
    /// Active collaborations in this period
    pub active_collaborations: u32,
    /// New collaborations formed
    pub new_collaborations: u32,
}

/// Topic model for research areas
#[derive(Debug, Clone)]
pub struct TopicModel {
    /// Topic ID
    pub topic_id: String,
    /// Topic name/label
    pub topic_name: String,
    /// Topic words with probabilities
    pub topic_words: Vec<(String, f64)>,
    /// Document-topic distribution
    pub document_topics: HashMap<String, f64>,
    /// Topic coherence score
    pub coherence_score: f64,
    /// Topic trend over time
    pub temporal_trend: Vec<TopicTrend>,
}

/// Topic trend over time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TopicTrend {
    /// Time period
    pub timestamp: DateTime<Utc>,
    /// Topic popularity/frequency
    pub popularity: f64,
    /// Number of publications in this topic
    pub publication_count: u64,
    /// Topic growth rate
    pub growth_rate: f64,
}

/// Impact prediction model
#[derive(Debug, Clone)]
pub struct ImpactPredictor {
    /// Feature weights for impact prediction
    pub feature_weights: HashMap<String, f64>,
    /// Model performance metrics
    pub performance_metrics: PredictionMetrics,
    /// Last model update
    pub last_update: DateTime<Utc>,
}

/// Prediction performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionMetrics {
    /// Mean absolute error
    pub mae: f64,
    /// Root mean square error
    pub rmse: f64,
    /// R-squared score
    pub r2_score: f64,
    /// Precision at different thresholds
    pub precision_at_k: HashMap<u32, f64>,
}

impl ResearchNetworkAnalyzer {
    /// Create new research network analyzer
    pub fn new(config: ResearchNetworkConfig) -> Self {
        Self {
            author_embeddings: Arc::new(RwLock::new(HashMap::new())),
            publication_embeddings: Arc::new(RwLock::new(HashMap::new())),
            citation_network: Arc::new(RwLock::new(CitationNetwork {
                citations: HashMap::new(),
                co_citations: HashMap::new(),
                bibliographic_coupling: HashMap::new(),
                temporal_patterns: HashMap::new(),
            })),
            collaboration_network: Arc::new(RwLock::new(CollaborationNetwork {
                collaborations: HashMap::new(),
                research_communities: Vec::new(),
                temporal_collaborations: HashMap::new(),
            })),
            topic_models: Arc::new(RwLock::new(HashMap::new())),
            config,
            analysis_tasks: Vec::new(),
        }
    }

    /// Start background analysis tasks
    pub async fn start(&mut self) -> Result<()> {
        info!("Starting research network analysis system");

        // Start citation network analysis task
        let citation_task = self.start_citation_analysis().await;
        self.analysis_tasks.push(citation_task);

        // Start collaboration analysis task
        let collaboration_task = self.start_collaboration_analysis().await;
        self.analysis_tasks.push(collaboration_task);

        // Start impact prediction task
        let impact_task = self.start_impact_prediction().await;
        self.analysis_tasks.push(impact_task);

        // Start topic modeling task
        let topic_task = self.start_topic_modeling().await;
        self.analysis_tasks.push(topic_task);

        info!("Research network analysis system started successfully");
        Ok(())
    }

    /// Stop analysis tasks
    pub async fn stop(&mut self) {
        info!("Stopping research network analysis system");

        for task in self.analysis_tasks.drain(..) {
            task.abort();
        }

        info!("Research network analysis system stopped");
    }

    /// Generate author embedding based on publications and collaborations
    pub async fn generate_author_embedding(&self, author_id: &str) -> Result<AuthorEmbedding> {
        // Check if already computed
        {
            let embeddings = self
                .author_embeddings
                .read()
                .expect("rwlock should not be poisoned");
            if let Some(existing) = embeddings.get(author_id) {
                return Ok(existing.clone());
            }
        }

        info!("Generating author embedding for: {}", author_id);

        // Collect author's publications
        let author_publications = self.get_author_publications(author_id).await?;

        // Get collaboration information
        let collaborations = self.get_author_collaborations(author_id).await?;

        // Compute research topics
        let research_topics = self
            .extract_author_topics(author_id, &author_publications)
            .await?;

        // Calculate metrics
        let h_index = self.calculate_h_index(&author_publications).await?;
        let citation_count = author_publications.iter().map(|p| p.citation_count).sum();
        let collaboration_score = self.calculate_collaboration_score(&collaborations).await?;
        let impact_score = self.calculate_author_impact_score(author_id).await?;

        // Generate embedding vector
        let embedding = self
            .compute_author_embedding_vector(
                &author_publications,
                &collaborations,
                &research_topics,
            )
            .await?;

        // Determine career stage
        let career_stage = self
            .classify_career_stage(citation_count, author_publications.len() as u64, h_index)
            .await?;

        let author_embedding = AuthorEmbedding {
            author_id: author_id.to_string(),
            name: format!("Author_{author_id}"), // Placeholder - would get from database
            affiliations: vec!["Unknown".to_string()], // Placeholder
            research_topics,
            h_index,
            citation_count,
            publication_count: author_publications.len() as u64,
            embedding,
            collaboration_score,
            impact_score,
            career_stage,
            last_updated: Utc::now(),
        };

        // Cache the result
        {
            let mut embeddings = self
                .author_embeddings
                .write()
                .expect("rwlock should not be poisoned");
            embeddings.insert(author_id.to_string(), author_embedding.clone());
        }

        info!(
            "Generated author embedding for {} with h-index: {:.2}",
            author_id, h_index
        );
        Ok(author_embedding)
    }

    /// Generate publication embedding based on content and citations
    pub async fn generate_publication_embedding(
        &self,
        publication_id: &str,
    ) -> Result<PublicationEmbedding> {
        // Check if already computed
        {
            let embeddings = self
                .publication_embeddings
                .read()
                .expect("rwlock should not be poisoned");
            if let Some(existing) = embeddings.get(publication_id) {
                return Ok(existing.clone());
            }
        }

        info!("Generating publication embedding for: {}", publication_id);

        // Get publication metadata (would come from database)
        let title = format!("Publication_{publication_id}");
        let abstract_text = format!("Abstract for publication {publication_id}");
        let authors = vec![format!("author_{}", publication_id)];
        let venue = "Unknown Venue".to_string();
        let year = 2023; // Placeholder
        let doi = Some(format!("10.1000/{publication_id}"));

        // Get citation information
        let citation_count = self.get_publication_citation_count(publication_id).await?;

        // Extract topics
        let topic_distribution = self
            .extract_publication_topics(publication_id, &abstract_text)
            .await?;

        // Generate content embedding
        let embedding = self
            .compute_publication_embedding_vector(&title, &abstract_text, &topic_distribution)
            .await?;

        // Predict impact
        let predicted_impact = self
            .predict_publication_impact(citation_count, &topic_distribution, &embedding)
            .await?;

        let publication_embedding = PublicationEmbedding {
            publication_id: publication_id.to_string(),
            title,
            abstract_text,
            authors,
            venue,
            year,
            citation_count,
            topic_distribution,
            embedding,
            predicted_impact,
            publication_type: PublicationType::JournalArticle, // Default
            doi,
            last_updated: Utc::now(),
        };

        // Cache the result
        {
            let mut embeddings = self
                .publication_embeddings
                .write()
                .expect("rwlock should not be poisoned");
            embeddings.insert(publication_id.to_string(), publication_embedding.clone());
        }

        info!(
            "Generated publication embedding for {} with predicted impact: {:.3}",
            publication_id, predicted_impact
        );
        Ok(publication_embedding)
    }

    /// Analyze citation patterns and relationships
    pub async fn analyze_citation_patterns(&self, publication_id: &str) -> Result<Vec<Citation>> {
        let network = self
            .citation_network
            .read()
            .expect("rwlock should not be poisoned");

        if let Some(citations) = network.citations.get(publication_id) {
            Ok(citations.clone())
        } else {
            Ok(Vec::new())
        }
    }

    /// Find similar authors based on research interests and collaboration patterns
    pub async fn find_similar_authors(
        &self,
        author_id: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let target_embedding = self.generate_author_embedding(author_id).await?;
        let embeddings_data: Vec<(String, AuthorEmbedding)> = {
            let embeddings = self
                .author_embeddings
                .read()
                .expect("rwlock should not be poisoned");
            embeddings
                .iter()
                .filter(|(other_id, _)| *other_id != author_id)
                .map(|(id, emb)| (id.clone(), emb.clone()))
                .collect()
        };

        let mut similarities = Vec::new();

        for (other_id, other_embedding) in embeddings_data {
            let similarity = self
                .calculate_author_similarity(&target_embedding, &other_embedding)
                .await?;
            similarities.push((other_id, similarity));
        }

        // Sort by similarity and take top k
        similarities.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("similarity scores should be comparable")
        });
        similarities.truncate(k);

        Ok(similarities)
    }

    /// Predict research impact for a publication
    pub async fn predict_research_impact(&self, publication_id: &str) -> Result<f64> {
        let publication = self.generate_publication_embedding(publication_id).await?;
        Ok(publication.predicted_impact)
    }

    /// Analyze research trends over time
    pub async fn analyze_research_trends(
        &self,
        topic: &str,
        years: u32,
    ) -> Result<Vec<TopicTrend>> {
        let topics = self
            .topic_models
            .read()
            .expect("rwlock should not be poisoned");

        if let Some(topic_model) = topics.get(topic) {
            // Filter trends for the specified time period
            let cutoff_date = Utc::now() - chrono::Duration::days((years * 365) as i64);
            let recent_trends: Vec<TopicTrend> = topic_model
                .temporal_trend
                .iter()
                .filter(|trend| trend.timestamp > cutoff_date)
                .cloned()
                .collect();

            Ok(recent_trends)
        } else {
            Ok(Vec::new())
        }
    }

    /// Get research communities/clusters
    pub async fn get_research_communities(&self) -> Result<Vec<ResearchCommunity>> {
        let network = self
            .collaboration_network
            .read()
            .expect("rwlock should not be poisoned");
        Ok(network.research_communities.clone())
    }

    /// Update citation network with new citation
    pub async fn add_citation(&self, citation: Citation) -> Result<()> {
        let mut network = self
            .citation_network
            .write()
            .expect("rwlock should not be poisoned");

        network
            .citations
            .entry(citation.citing_paper.clone())
            .or_default()
            .push(citation);

        info!("Added new citation to network");
        Ok(())
    }

    // ===== PRIVATE HELPER METHODS =====

    async fn get_author_publications(&self, _author_id: &str) -> Result<Vec<PublicationEmbedding>> {
        // Placeholder - would query database
        Ok(Vec::new())
    }

    async fn get_author_collaborations(&self, _author_id: &str) -> Result<Vec<Collaboration>> {
        // Placeholder - would query collaboration network
        Ok(Vec::new())
    }

    async fn extract_author_topics(
        &self,
        _author_id: &str,
        _publications: &[PublicationEmbedding],
    ) -> Result<Vec<String>> {
        // Placeholder - would perform topic extraction
        Ok(vec![
            "machine_learning".to_string(),
            "natural_language_processing".to_string(),
        ])
    }

    async fn calculate_h_index(&self, publications: &[PublicationEmbedding]) -> Result<f64> {
        let mut citation_counts: Vec<u64> = publications.iter().map(|p| p.citation_count).collect();

        citation_counts.sort_by(|a, b| b.cmp(a));

        let mut h_index = 0;
        for (i, &citations) in citation_counts.iter().enumerate() {
            if citations >= (i + 1) as u64 {
                h_index = i + 1;
            } else {
                break;
            }
        }

        Ok(h_index as f64)
    }

    async fn calculate_collaboration_score(&self, collaborations: &[Collaboration]) -> Result<f64> {
        if collaborations.is_empty() {
            return Ok(0.0);
        }

        let total_strength: f64 = collaborations.iter().map(|c| c.strength).sum();
        Ok(total_strength / collaborations.len() as f64)
    }

    async fn calculate_author_impact_score(&self, _author_id: &str) -> Result<f64> {
        // Placeholder - would calculate based on citations, h-index, collaboration network position
        Ok(0.75)
    }

    async fn compute_author_embedding_vector(
        &self,
        _publications: &[PublicationEmbedding],
        _collaborations: &[Collaboration],
        _topics: &[String],
    ) -> Result<Vector> {
        // Placeholder - would compute actual embedding
        let values = (0..self.config.embedding_dimension)
            .map(|_| {
                let mut random = Random::default();
                random.random::<f32>()
            })
            .collect();
        Ok(Vector::new(values))
    }

    async fn classify_career_stage(
        &self,
        citation_count: u64,
        publication_count: u64,
        h_index: f64,
    ) -> Result<CareerStage> {
        if citation_count < 100 && publication_count < 10 && h_index < 5.0 {
            Ok(CareerStage::EarlyCareer)
        } else if citation_count < 1000 && publication_count < 50 && h_index < 20.0 {
            Ok(CareerStage::MidCareer)
        } else if citation_count >= 1000 || publication_count >= 50 || h_index >= 20.0 {
            Ok(CareerStage::SeniorCareer)
        } else {
            Ok(CareerStage::Unknown)
        }
    }

    async fn get_publication_citation_count(&self, _publication_id: &str) -> Result<u64> {
        // Placeholder - would query citation database
        let mut random = Random::default();
        Ok(random.random::<u64>() % 100)
    }

    async fn extract_publication_topics(
        &self,
        _publication_id: &str,
        _abstract_text: &str,
    ) -> Result<Vec<f64>> {
        // Placeholder - would perform topic modeling
        let num_topics = self.config.topic_config.num_topics;
        let mut distribution = vec![0.0; num_topics];

        // Generate random distribution that sums to 1.0
        let total: f64 = (0..num_topics)
            .map(|_| {
                let mut random = Random::default();
                random.random::<f64>()
            })
            .sum();
        for item in distribution.iter_mut().take(num_topics) {
            let mut random = Random::default();
            *item = random.random::<f64>() / total;
        }

        Ok(distribution)
    }

    async fn compute_publication_embedding_vector(
        &self,
        _title: &str,
        _abstract_text: &str,
        _topic_distribution: &[f64],
    ) -> Result<Vector> {
        // Placeholder - would compute actual embedding
        let values = (0..self.config.embedding_dimension)
            .map(|_| {
                let mut random = Random::default();
                random.random::<f32>()
            })
            .collect();
        Ok(Vector::new(values))
    }

    async fn predict_publication_impact(
        &self,
        citation_count: u64,
        _topic_distribution: &[f64],
        _embedding: &Vector,
    ) -> Result<f64> {
        // Placeholder - would use trained impact prediction model
        let base_impact = (citation_count as f64).ln() / 10.0;
        Ok(base_impact.clamp(0.0, 1.0))
    }

    async fn calculate_author_similarity(
        &self,
        author1: &AuthorEmbedding,
        author2: &AuthorEmbedding,
    ) -> Result<f64> {
        // Calculate cosine similarity between embeddings
        let embedding1 = &author1.embedding.values;
        let embedding2 = &author2.embedding.values;

        let dot_product: f32 = embedding1
            .iter()
            .zip(embedding2.iter())
            .map(|(a, b)| a * b)
            .sum();
        let norm1: f32 = embedding1.iter().map(|x| x * x).sum::<f32>().sqrt();
        let norm2: f32 = embedding2.iter().map(|x| x * x).sum::<f32>().sqrt();

        let cosine_similarity = if norm1 > 0.0 && norm2 > 0.0 {
            dot_product / (norm1 * norm2)
        } else {
            0.0
        };

        // Combine with topic similarity
        let topic_similarity = self
            .calculate_topic_similarity(&author1.research_topics, &author2.research_topics)
            .await?;

        // Weighted combination
        let final_similarity = 0.7 * cosine_similarity as f64 + 0.3 * topic_similarity;

        Ok(final_similarity)
    }

    async fn calculate_topic_similarity(
        &self,
        topics1: &[String],
        topics2: &[String],
    ) -> Result<f64> {
        let set1: HashSet<_> = topics1.iter().collect();
        let set2: HashSet<_> = topics2.iter().collect();

        let intersection = set1.intersection(&set2).count();
        let union = set1.union(&set2).count();

        if union > 0 {
            Ok(intersection as f64 / union as f64)
        } else {
            Ok(0.0)
        }
    }

    // ===== BACKGROUND ANALYSIS TASKS =====

    async fn start_citation_analysis(&self) -> JoinHandle<()> {
        let _citation_network = Arc::clone(&self.citation_network);
        let interval =
            std::time::Duration::from_secs(self.config.citation_update_interval_hours * 3600);

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Perform citation network analysis
                info!("Performing citation network analysis");

                // Placeholder for actual analysis
                // Would analyze citation patterns, identify influential papers, etc.

                debug!("Citation network analysis completed");
            }
        })
    }

    async fn start_collaboration_analysis(&self) -> JoinHandle<()> {
        let _collaboration_network = Arc::clone(&self.collaboration_network);
        let interval = std::time::Duration::from_secs(
            self.config.collaboration_analysis_interval_hours * 3600,
        );

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Perform collaboration network analysis
                info!("Performing collaboration network analysis");

                // Placeholder for actual analysis
                // Would detect research communities, analyze collaboration patterns, etc.

                debug!("Collaboration network analysis completed");
            }
        })
    }

    async fn start_impact_prediction(&self) -> JoinHandle<()> {
        let interval =
            std::time::Duration::from_secs(self.config.impact_prediction_refresh_hours * 3600);

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Refresh impact prediction models
                info!("Refreshing impact prediction models");

                // Placeholder for actual model training/updating
                // Would retrain models based on recent citation data

                debug!("Impact prediction models refreshed");
            }
        })
    }

    async fn start_topic_modeling(&self) -> JoinHandle<()> {
        let topic_models = Arc::clone(&self.topic_models);
        let _config = self.config.clone();
        let interval = std::time::Duration::from_secs(24 * 3600); // Daily

        tokio::spawn(async move {
            let mut interval_timer = tokio::time::interval(interval);

            loop {
                interval_timer.tick().await;

                // Update topic models
                info!("Updating topic models");

                // Create sample topic model
                let topic_model = TopicModel {
                    topic_id: "machine_learning".to_string(),
                    topic_name: "Machine Learning".to_string(),
                    topic_words: vec![
                        ("neural".to_string(), 0.1),
                        ("network".to_string(), 0.09),
                        ("learning".to_string(), 0.08),
                        ("algorithm".to_string(), 0.07),
                        ("model".to_string(), 0.06),
                    ],
                    document_topics: HashMap::new(),
                    coherence_score: 0.75,
                    temporal_trend: vec![
                        TopicTrend {
                            timestamp: Utc::now() - chrono::Duration::days(365),
                            popularity: 0.6,
                            publication_count: 1000,
                            growth_rate: 0.15,
                        },
                        TopicTrend {
                            timestamp: Utc::now(),
                            popularity: 0.8,
                            publication_count: 1500,
                            growth_rate: 0.25,
                        },
                    ],
                };

                {
                    let mut models = topic_models.write().expect("rwlock should not be poisoned");
                    models.insert("machine_learning".to_string(), topic_model);
                }

                debug!("Topic models updated");
            }
        })
    }
}

/// Research network metrics and statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkMetrics {
    /// Total number of authors
    pub total_authors: usize,
    /// Total number of publications
    pub total_publications: usize,
    /// Total number of citations
    pub total_citations: u64,
    /// Average citations per paper
    pub avg_citations_per_paper: f64,
    /// Network density
    pub network_density: f64,
    /// Clustering coefficient
    pub clustering_coefficient: f64,
    /// Average path length
    pub average_path_length: f64,
    /// Most influential authors
    pub top_authors: Vec<String>,
    /// Trending topics
    pub trending_topics: Vec<String>,
}

impl ResearchNetworkAnalyzer {
    /// Get comprehensive network metrics
    pub async fn get_network_metrics(&self) -> Result<NetworkMetrics> {
        let author_embeddings = self
            .author_embeddings
            .read()
            .expect("rwlock should not be poisoned");
        let publication_embeddings = self
            .publication_embeddings
            .read()
            .expect("rwlock should not be poisoned");

        let total_authors = author_embeddings.len();
        let total_publications = publication_embeddings.len();
        let total_citations = publication_embeddings
            .values()
            .map(|p| p.citation_count)
            .sum();

        let avg_citations_per_paper = if total_publications > 0 {
            total_citations as f64 / total_publications as f64
        } else {
            0.0
        };

        // Get top authors by impact score
        let mut author_scores: Vec<_> = author_embeddings
            .iter()
            .map(|(id, embedding)| (id.clone(), embedding.impact_score))
            .collect();
        author_scores.sort_by(|a, b| {
            b.1.partial_cmp(&a.1)
                .expect("similarity scores should be comparable")
        });
        let top_authors: Vec<String> = author_scores
            .into_iter()
            .take(10)
            .map(|(id, _)| id)
            .collect();

        Ok(NetworkMetrics {
            total_authors,
            total_publications,
            total_citations,
            avg_citations_per_paper,
            network_density: 0.1,        // Placeholder
            clustering_coefficient: 0.3, // Placeholder
            average_path_length: 4.5,    // Placeholder
            top_authors,
            trending_topics: vec!["machine_learning".to_string(), "deep_learning".to_string()],
        })
    }
}

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

    #[tokio::test]
    async fn test_research_network_analyzer_creation() {
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        // Test that analyzer is created successfully
        assert_eq!(
            analyzer
                .author_embeddings
                .read()
                .expect("rwlock should not be poisoned")
                .len(),
            0
        );
        assert_eq!(
            analyzer
                .publication_embeddings
                .read()
                .expect("rwlock should not be poisoned")
                .len(),
            0
        );
    }

    #[tokio::test]
    async fn test_author_embedding_generation() {
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        let result = analyzer.generate_author_embedding("test_author").await;
        assert!(result.is_ok());

        let embedding = result.expect("should succeed");
        assert_eq!(embedding.author_id, "test_author");
        assert!(embedding.h_index >= 0.0);
        assert_eq!(embedding.embedding.values.len(), 512); // Default dimension
    }

    #[tokio::test]
    async fn test_publication_embedding_generation() {
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        let result = analyzer
            .generate_publication_embedding("test_publication")
            .await;
        assert!(result.is_ok());

        let embedding = result.expect("should succeed");
        assert_eq!(embedding.publication_id, "test_publication");
        assert!(embedding.predicted_impact >= 0.0);
        assert!(embedding.predicted_impact <= 1.0);
    }

    #[tokio::test]
    async fn test_h_index_calculation() {
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        // Create test publications with different citation counts
        let publications = vec![
            PublicationEmbedding {
                publication_id: "p1".to_string(),
                title: "Test 1".to_string(),
                abstract_text: "Abstract 1".to_string(),
                authors: vec!["author1".to_string()],
                venue: "Venue 1".to_string(),
                year: 2023,
                citation_count: 10,
                topic_distribution: vec![],
                embedding: Vector::new(vec![]),
                predicted_impact: 0.5,
                publication_type: PublicationType::JournalArticle,
                doi: None,
                last_updated: Utc::now(),
            },
            PublicationEmbedding {
                publication_id: "p2".to_string(),
                title: "Test 2".to_string(),
                abstract_text: "Abstract 2".to_string(),
                authors: vec!["author1".to_string()],
                venue: "Venue 2".to_string(),
                year: 2023,
                citation_count: 5,
                topic_distribution: vec![],
                embedding: Vector::new(vec![]),
                predicted_impact: 0.3,
                publication_type: PublicationType::JournalArticle,
                doi: None,
                last_updated: Utc::now(),
            },
        ];

        let h_index = analyzer
            .calculate_h_index(&publications)
            .await
            .expect("should succeed");
        assert_eq!(h_index, 2.0); // Both papers have at least 2 citations
    }

    #[test]
    fn test_career_stage_classification() {
        // Test early career
        let rt = tokio::runtime::Runtime::new().expect("should succeed");
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        let stage = rt
            .block_on(analyzer.classify_career_stage(50, 5, 3.0))
            .expect("should succeed");
        assert!(matches!(stage, CareerStage::EarlyCareer));

        // Test senior career
        let stage = rt
            .block_on(analyzer.classify_career_stage(2000, 100, 25.0))
            .expect("should succeed");
        assert!(matches!(stage, CareerStage::SeniorCareer));
    }

    #[tokio::test]
    async fn test_network_metrics() {
        let config = ResearchNetworkConfig::default();
        let analyzer = ResearchNetworkAnalyzer::new(config);

        // Add some test data
        let _author_embedding = analyzer
            .generate_author_embedding("test_author")
            .await
            .expect("should succeed");
        let _publication_embedding = analyzer
            .generate_publication_embedding("test_publication")
            .await
            .expect("should succeed");

        let metrics = analyzer
            .get_network_metrics()
            .await
            .expect("should succeed");
        assert_eq!(metrics.total_authors, 1);
        assert_eq!(metrics.total_publications, 1);
    }
}