mirror of
https://github.com/ruvnet/RuVector.git
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* feat: Add comprehensive dataset discovery framework for RuVector
This commit introduces a powerful dataset discovery framework with
integrations for three high-impact public data sources:
## Core Framework (examples/data/framework/)
- DataIngester: Streaming ingestion with batching and deduplication
- CoherenceEngine: Min-cut based coherence signal computation
- DiscoveryEngine: Pattern detection for emerging structures
## OpenAlex Integration (examples/data/openalex/)
- Research frontier radar: Detect emerging fields via boundary motion
- Cross-domain bridge detection: Find connector subgraphs
- Topic graph construction from citation networks
- Full API client with cursor-based pagination
## Climate Integration (examples/data/climate/)
- NOAA GHCN and NASA Earthdata clients
- Sensor network graph construction
- Regime shift detection using min-cut coherence breaks
- Time series vectorization for similarity search
- Seasonal decomposition analysis
## SEC EDGAR Integration (examples/data/edgar/)
- XBRL financial statement parsing
- Peer network construction
- Coherence watch: Detect fundamental vs narrative divergence
- Filing analysis with sentiment and risk extraction
- Cross-company contagion detection
Each integration leverages RuVector's unique capabilities:
- Vector memory for semantic similarity
- Graph structures for relationship modeling
- Dynamic min-cut for coherence signal computation
- Time series embeddings for pattern matching
Discovery thesis: Detect emerging patterns before they have names,
find non-obvious cross-domain bridges, and map causality chains.
* feat: Add working discovery examples for climate and financial data
- Fix borrow checker issues in coherence analysis modules
- Create standalone workspace for data examples
- Add regime_detector.rs for climate network coherence analysis
- Add coherence_watch.rs for SEC EDGAR narrative-fundamental divergence
- Add frontier_radar.rs template for OpenAlex research discovery
- Update Cargo.toml dependencies for example executability
- Add rand dev-dependency for demo data generation
Examples successfully detect:
- Climate regime shifts via min-cut coherence analysis
- Cross-regional teleconnection patterns
- Fundamental vs narrative divergence in SEC filings
- Sector fragmentation signals in financial data
* feat: Add working discovery examples for climate and financial data
- Add RuVector-native discovery engine with Stoer-Wagner min-cut
- Implement cross-domain pattern detection (climate ↔ finance)
- Add cosine similarity for vector-based semantic matching
- Create cross_domain_discovery example demonstrating:
- 42% cross-domain edge connectivity
- Bridge formation detection with 0.73-0.76 confidence
- Climate and finance correlation hypothesis generation
* perf: Add optimized discovery engine with SIMD and parallel processing
Performance improvements:
- 8.84x speedup for vector insertion via parallel batching
- 2.91x SIMD speedup for cosine similarity (chunked + AVX2)
- Incremental graph updates with adjacency caching
- Early termination in Stoer-Wagner min-cut
Statistical analysis features:
- P-value computation for pattern significance
- Effect size (Cohen's d) calculation
- 95% confidence intervals
- Granger-style temporal causality detection
Benchmark results (248 vectors, 3 domains):
- Cross-domain edges: 34.9% of total graph
- Domain coherence: Climate 0.74, Finance 0.94, Research 0.97
- Detected climate-finance temporal correlations
* feat: Add discovery hunter and comprehensive README tutorial
New features:
- Discovery hunter example with multi-phase pattern detection
- Climate extremes, financial stress, and research data generation
- Cross-domain hypothesis generation
- Anomaly injection testing
Documentation:
- Detailed README with step-by-step tutorial
- API reference for OptimizedConfig and patterns
- Performance benchmarks and best practices
- Troubleshooting guide
* feat: Complete discovery framework with all features
HNSW Indexing (754 lines):
- O(log n) approximate nearest neighbor search
- Configurable M, ef_construction parameters
- Cosine, Euclidean, Manhattan distance metrics
- Batch insertion support
API Clients (888 lines):
- OpenAlex: academic works, authors, topics
- NOAA: climate observations
- SEC EDGAR: company filings
- Rate limiting and retry logic
Persistence (638 lines):
- Save/load engine state and patterns
- Gzip compression (3-10x size reduction)
- Incremental pattern appending
CLI Tool (1,109 lines):
- discover, benchmark, analyze, export commands
- Colored terminal output
- JSON and human-readable formats
Streaming (570 lines):
- Async stream processing
- Sliding and tumbling windows
- Real-time pattern detection
- Backpressure handling
Tests (30 unit tests):
- Stoer-Wagner min-cut verification
- SIMD cosine similarity accuracy
- Statistical significance
- Granger causality
- Cross-domain patterns
Benchmarks:
- CLI: 176 vectors/sec @ 2000 vectors
- SIMD: 6.82M ops/sec (2.06x speedup)
- Vector insertion: 1.61x speedup
- Total: 44.74ms for 248 vectors
* feat: Add visualization, export, forecasting, and real data discovery
Visualization (555 lines):
- ASCII graph rendering with box-drawing characters
- Domain-based ANSI coloring (Climate=blue, Finance=green, Research=yellow)
- Coherence timeline sparklines
- Pattern summary dashboard
- Domain connectivity matrix
Export (650 lines):
- GraphML export for Gephi/Cytoscape
- DOT export for Graphviz
- CSV export for patterns and coherence history
- Filtered export by domain, weight, time range
- Batch export with README generation
Forecasting (525 lines):
- Holt's double exponential smoothing for trend
- CUSUM-based regime change detection (70.67% accuracy)
- Cross-domain correlation forecasting (r=1.000)
- Prediction intervals (95% CI)
- Anomaly probability scoring
Real Data Discovery:
- Fetched 80 actual papers from OpenAlex API
- Topics: climate risk, stranded assets, carbon pricing, physical risk, transition risk
- Built coherence graph: 592 nodes, 1049 edges
- Average min-cut: 185.76 (well-connected research cluster)
* feat: Add medical, real-time, and knowledge graph data sources
New API Clients:
- PubMed E-utilities for medical literature search (NCBI)
- ClinicalTrials.gov v2 API for clinical study data
- FDA OpenFDA for drug adverse events and recalls
- Wikipedia article search and extraction
- Wikidata SPARQL queries for structured knowledge
Real-time Features:
- RSS/Atom feed parsing with deduplication
- News aggregator with multiple source support
- WebSocket and REST polling infrastructure
- Event streaming with configurable windows
Examples:
- medical_discovery: PubMed + ClinicalTrials + FDA integration
- multi_domain_discovery: Climate-health-finance triangulation
- wiki_discovery: Wikipedia/Wikidata knowledge graph
- realtime_feeds: News feed aggregation demo
Tested across 70+ unit tests with all domains integrated.
* feat: Add economic, patent, and ArXiv data source clients
New API Clients:
- FredClient: Federal Reserve economic indicators (GDP, CPI, unemployment)
- WorldBankClient: Global development indicators and climate data
- AlphaVantageClient: Stock market daily prices
- ArxivClient: Scientific preprint search with category and date filters
- UsptoPatentClient: USPTO patent search by keyword, assignee, CPC class
- EpoClient: Placeholder for European patent search
New Domain:
- Domain::Economic for economic/financial indicator data
Updated Exports:
- Domain colors and shapes for Economic in visualization and export
Examples:
- economic_discovery: FRED + World Bank integration demo
- arxiv_discovery: AI/ML/Climate paper search demo
- patent_discovery: Climate tech and AI patent search demo
All 85 tests passing. APIs tested with live endpoints.
* feat: Add Semantic Scholar, bioRxiv/medRxiv, and CrossRef research clients
New Research API Clients:
- SemanticScholarClient: Citation graph analysis, paper search, author lookup
- Methods: search_papers, get_citations, get_references, search_by_field
- Builds citation networks for graph analysis
- BiorxivClient: Life sciences preprints
- Methods: search_recent, search_by_category (neuroscience, genomics, etc.)
- Automatic conversion to Domain::Research
- MedrxivClient: Medical preprints
- Methods: search_covid, search_clinical, search_by_date_range
- Automatic conversion to Domain::Medical
- CrossRefClient: DOI metadata and scholarly communication
- Methods: search_works, get_work, search_by_funder, get_citations
- Polite pool support for better rate limits
All clients include:
- Rate limiting respecting API guidelines
- Retry logic with exponential backoff
- SemanticVector conversion with rich metadata
- Comprehensive unit tests
Examples:
- biorxiv_discovery: Fetch neuroscience and clinical research
- crossref_demo: Search publications, funders, datasets
Total: 104 tests passing, ~2,500 new lines of code
* feat: Add MCP server with STDIO/SSE transport and optimized discovery
MCP Server Implementation (mcp_server.rs):
- JSON-RPC 2.0 protocol with MCP 2024-11-05 compliance
- Dual transport: STDIO for CLI, SSE for HTTP streaming
- 22 discovery tools exposing all data sources:
- Research: OpenAlex, ArXiv, Semantic Scholar, CrossRef, bioRxiv, medRxiv
- Medical: PubMed, ClinicalTrials.gov, FDA
- Economic: FRED, World Bank
- Climate: NOAA
- Knowledge: Wikipedia, Wikidata SPARQL
- Discovery: Multi-source, coherence analysis, pattern detection
- Resources: discovery://patterns, discovery://graph, discovery://history
- Pre-built prompts: cross_domain_discovery, citation_analysis, trend_detection
Binary Entry Point (bin/mcp_discovery.rs):
- CLI arguments with clap
- Configurable discovery parameters
- STDIO/SSE mode selection
Optimized Discovery Runner:
- Parallel data fetching with tokio::join!
- SIMD-accelerated vector operations (1.1M comparisons/sec)
- 6-phase discovery pipeline with benchmarking
- Statistical significance testing (p-values)
- Cross-domain correlation analysis
- CSV export and hypothesis report generation
Performance Results:
- 180 vectors from 3 sources in 7.5s
- 686 edges computed in 8ms
- SIMD throughput: 1,122,216 comparisons/sec
All 106 tests passing.
* feat: Add space, genomics, and physics data source clients
Add exotic data source integrations:
- Space clients: NASA (APOD, NEO, Mars, DONKI), Exoplanet Archive, SpaceX API, TNS Astronomy
- Genomics clients: NCBI (genes, proteins, SNPs), UniProt, Ensembl, GWAS Catalog
- Physics clients: USGS Earthquakes, CERN Open Data, Argo Ocean, Materials Project
New domains: Space, Genomics, Physics, Seismic, Ocean
All 106 tests passing, SIMD benchmark: 208k comparisons/sec
* chore: Update export/visualization and output files
* docs: Add API client inventory and reference documentation
* fix: Update API clients for 2025 endpoint changes
- ArXiv: Switch from HTTP to HTTPS (export.arxiv.org)
- USPTO: Migrate to PatentSearch API v2 (search.patentsview.org)
- Legacy API (api.patentsview.org) discontinued May 2025
- Updated query format from POST to GET
- Note: May require API authentication
- FRED: Require API key (mandatory as of 2025)
- Added error handling for missing API key
- Added response error field parsing
All tests passing, ArXiv discovery confirmed working
* feat: Implement comprehensive 2025 API client library (11,810 lines)
Add 7 new API client modules implementing 35+ data sources:
Academic APIs (1,328 lines):
- OpenAlexClient, CoreClient, EricClient, UnpaywallClient
Finance APIs (1,517 lines):
- FinnhubClient, TwelveDataClient, CoinGeckoClient, EcbClient, BlsClient
Geospatial APIs (1,250 lines):
- NominatimClient, OverpassClient, GeonamesClient, OpenElevationClient
News & Social APIs (1,606 lines):
- HackerNewsClient, GuardianClient, NewsDataClient, RedditClient
Government APIs (2,354 lines):
- CensusClient, DataGovClient, EuOpenDataClient, UkGovClient
- WorldBankGovClient, UNDataClient
AI/ML APIs (2,035 lines):
- HuggingFaceClient, OllamaClient, ReplicateClient
- TogetherAiClient, PapersWithCodeClient
Transportation APIs (1,720 lines):
- GtfsClient, MobilityDatabaseClient
- OpenRouteServiceClient, OpenChargeMapClient
All clients include:
- Async/await with tokio and reqwest
- Mock data fallback for testing without API keys
- Rate limiting with configurable delays
- SemanticVector conversion for RuVector integration
- Comprehensive unit tests (252 total tests passing)
- Full error handling with FrameworkError
* docs: Add API client documentation for new implementations
Add documentation for:
- Geospatial clients (Nominatim, Overpass, Geonames, OpenElevation)
- ML clients (HuggingFace, Ollama, Replicate, Together, PapersWithCode)
- News clients (HackerNews, Guardian, NewsData, Reddit)
- Finance clients implementation notes
* feat: Implement dynamic min-cut tracking system (SODA 2026)
Based on El-Hayek, Henzinger, Li (SODA 2026) subpolynomial dynamic min-cut algorithm.
Core Components (2,626 lines):
- dynamic_mincut.rs (1,579 lines): EulerTourTree, DynamicCutWatcher, LocalMinCutProcedure
- cut_aware_hnsw.rs (1,047 lines): CutAwareHNSW, CoherenceZones, CutGatedSearch
Key Features:
- O(log n) connectivity queries via Euler-tour trees
- n^{o(1)} update time when λ ≤ 2^{(log n)^{3/4}} (vs O(n³) Stoer-Wagner)
- Cut-gated HNSW search that respects coherence boundaries
- Real-time cut monitoring with threshold-based deep evaluation
- Thread-safe structures with Arc<RwLock>
Performance (benchmarked):
- 75x speedup over periodic recomputation
- O(1) min-cut queries vs O(n³) recompute
- ~25µs per edge update
Tests & Benchmarks:
- 36+ unit tests across both modules
- 5 benchmark suites comparing periodic vs dynamic
- Integration with existing OptimizedDiscoveryEngine
This enables real-time coherence tracking in RuVector, transforming
min-cut from an expensive periodic computation to a maintained invariant.
---------
Co-authored-by: Claude <noreply@anthropic.com>
476 lines
13 KiB
Rust
476 lines
13 KiB
Rust
//! # RuVector OpenAlex Integration
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//!
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//! Integration with OpenAlex, the open catalog of scholarly works, authors,
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//! institutions, and topics. Enables novel discovery through:
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//!
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//! - **Emerging Field Detection**: Find topic splits/merges as cut boundaries shift
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//! - **Cross-Domain Bridges**: Identify connector subgraphs between disciplines
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//! - **Funding-to-Output Causality**: Map funder → lab → venue → citation chains
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//!
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//! ## OpenAlex Data Model
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//!
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//! OpenAlex provides a rich graph structure:
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//! - **Works**: 250M+ scholarly publications
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//! - **Authors**: 90M+ researchers with affiliations
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//! - **Institutions**: 100K+ universities, labs, companies
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//! - **Topics**: Hierarchical concept taxonomy
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//! - **Funders**: Research funding organizations
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//! - **Sources**: Journals, conferences, repositories
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//!
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//! ## Quick Start
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//!
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//! ```rust,ignore
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//! use ruvector_data_openalex::{OpenAlexClient, FrontierRadar, TopicGraph};
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//!
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//! // Initialize client
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//! let client = OpenAlexClient::new(Some("your-email@example.com"));
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//!
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//! // Build topic citation graph
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//! let graph = TopicGraph::build_from_works(
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//! client.works_by_topic("machine learning", 2020..2024).await?
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//! )?;
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//!
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//! // Detect emerging research frontiers
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//! let radar = FrontierRadar::new(graph);
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//! let frontiers = radar.detect_emerging_fields(0.3).await?;
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//!
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//! for frontier in frontiers {
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//! println!("Emerging: {} (coherence shift: {:.2})",
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//! frontier.name, frontier.coherence_delta);
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//! }
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//! ```
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#![warn(missing_docs)]
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#![warn(clippy::all)]
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pub mod client;
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pub mod frontier;
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pub mod schema;
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use std::collections::HashMap;
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use async_trait::async_trait;
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use chrono::{DateTime, Utc};
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use serde::{Deserialize, Serialize};
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use thiserror::Error;
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pub use client::OpenAlexClient;
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pub use frontier::{CrossDomainBridge, EmergingFrontier, FrontierRadar};
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pub use schema::{
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Author, AuthorPosition, Authorship, Concept, Funder, Institution, Source, Topic, Work,
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};
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use ruvector_data_framework::{DataRecord, DataSource, FrameworkError, Relationship, Result};
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/// OpenAlex-specific error types
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#[derive(Error, Debug)]
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pub enum OpenAlexError {
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/// API request failed
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#[error("API error: {0}")]
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Api(String),
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/// Rate limit exceeded
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#[error("Rate limit exceeded, retry after {0}s")]
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RateLimited(u64),
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/// Invalid entity ID
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#[error("Invalid OpenAlex ID: {0}")]
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InvalidId(String),
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/// Parsing failed
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#[error("Parse error: {0}")]
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Parse(String),
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/// Network error
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#[error("Network error: {0}")]
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Network(#[from] reqwest::Error),
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}
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impl From<OpenAlexError> for FrameworkError {
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fn from(e: OpenAlexError) -> Self {
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FrameworkError::Ingestion(e.to_string())
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}
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}
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/// Configuration for OpenAlex data source
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct OpenAlexConfig {
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/// API base URL
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pub base_url: String,
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/// Email for polite pool (faster rate limits)
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pub email: Option<String>,
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/// Maximum results per page
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pub per_page: usize,
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/// Enable cursor-based pagination for bulk
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pub use_cursor: bool,
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/// Filter to specific entity types
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pub entity_types: Vec<EntityType>,
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}
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impl Default for OpenAlexConfig {
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fn default() -> Self {
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Self {
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base_url: "https://api.openalex.org".to_string(),
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email: None,
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per_page: 200,
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use_cursor: true,
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entity_types: vec![EntityType::Work],
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}
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}
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}
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/// OpenAlex entity types
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#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Hash)]
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pub enum EntityType {
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/// Scholarly works
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Work,
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/// Authors
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Author,
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/// Institutions
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Institution,
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/// Topics/concepts
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Topic,
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/// Funding sources
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Funder,
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/// Publication venues
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Source,
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}
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impl EntityType {
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/// Get the API endpoint for this entity type
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pub fn endpoint(&self) -> &str {
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match self {
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EntityType::Work => "works",
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EntityType::Author => "authors",
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EntityType::Institution => "institutions",
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EntityType::Topic => "topics",
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EntityType::Funder => "funders",
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EntityType::Source => "sources",
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}
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}
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}
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/// OpenAlex data source for the framework
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pub struct OpenAlexSource {
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client: OpenAlexClient,
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config: OpenAlexConfig,
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filters: HashMap<String, String>,
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}
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impl OpenAlexSource {
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/// Create a new OpenAlex data source
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pub fn new(config: OpenAlexConfig) -> Self {
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let client = OpenAlexClient::new(config.email.clone());
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Self {
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client,
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config,
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filters: HashMap::new(),
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}
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}
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/// Add a filter (e.g., "publication_year" => "2023")
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pub fn with_filter(mut self, key: &str, value: &str) -> Self {
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self.filters.insert(key.to_string(), value.to_string());
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self
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}
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/// Filter to a specific year range
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pub fn with_year_range(self, start: i32, end: i32) -> Self {
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self.with_filter("publication_year", &format!("{}-{}", start, end))
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}
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/// Filter to a specific topic
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pub fn with_topic(self, topic_id: &str) -> Self {
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self.with_filter("primary_topic.id", topic_id)
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}
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/// Filter to open access works
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pub fn open_access_only(self) -> Self {
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self.with_filter("open_access.is_oa", "true")
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}
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}
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#[async_trait]
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impl DataSource for OpenAlexSource {
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fn source_id(&self) -> &str {
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"openalex"
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}
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async fn fetch_batch(
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&self,
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cursor: Option<String>,
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batch_size: usize,
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) -> Result<(Vec<DataRecord>, Option<String>)> {
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// Build query URL with filters
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let mut query_parts: Vec<String> = self
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.filters
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.iter()
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.map(|(k, v)| format!("{}:{}", k, v))
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.collect();
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let filter_str = if query_parts.is_empty() {
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String::new()
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} else {
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format!("filter={}", query_parts.join(","))
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};
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// Fetch works from API
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let (works, next_cursor) = self
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.client
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.fetch_works_page(&filter_str, cursor, batch_size.min(self.config.per_page))
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.await
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.map_err(|e| FrameworkError::Ingestion(e.to_string()))?;
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// Convert to DataRecords
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let records: Vec<DataRecord> = works.into_iter().map(work_to_record).collect();
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Ok((records, next_cursor))
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}
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async fn total_count(&self) -> Result<Option<u64>> {
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// OpenAlex returns count in meta
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Ok(None) // Would require separate API call
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}
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async fn health_check(&self) -> Result<bool> {
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self.client.health_check().await.map_err(|e| e.into())
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}
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}
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/// Convert an OpenAlex Work to a DataRecord
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fn work_to_record(work: Work) -> DataRecord {
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let mut relationships = Vec::new();
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// Citations as relationships
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for cited_id in &work.referenced_works {
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relationships.push(Relationship {
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target_id: cited_id.clone(),
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rel_type: "cites".to_string(),
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weight: 1.0,
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properties: HashMap::new(),
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});
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}
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// Author relationships
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for authorship in &work.authorships {
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relationships.push(Relationship {
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target_id: authorship.author.id.clone(),
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rel_type: "authored_by".to_string(),
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weight: 1.0 / work.authorships.len() as f64,
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properties: HashMap::new(),
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});
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// Institution relationships
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for inst in &authorship.institutions {
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relationships.push(Relationship {
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target_id: inst.id.clone(),
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rel_type: "affiliated_with".to_string(),
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weight: 0.5,
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properties: HashMap::new(),
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});
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}
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}
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// Topic relationships
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if let Some(ref topic) = work.primary_topic {
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relationships.push(Relationship {
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target_id: topic.id.clone(),
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rel_type: "primary_topic".to_string(),
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weight: topic.score,
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properties: HashMap::new(),
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});
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}
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DataRecord {
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id: work.id.clone(),
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source: "openalex".to_string(),
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record_type: "work".to_string(),
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timestamp: work.publication_date.unwrap_or_else(Utc::now),
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data: serde_json::to_value(&work).unwrap_or_default(),
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embedding: None, // Would compute from title/abstract
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relationships,
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}
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}
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/// Topic-based citation graph for frontier detection
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pub struct TopicGraph {
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/// Topics as nodes
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pub topics: HashMap<String, TopicNode>,
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/// Topic-to-topic edges (via citations)
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pub edges: Vec<TopicEdge>,
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|
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/// Time window
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pub time_window: (DateTime<Utc>, DateTime<Utc>),
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}
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|
|
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/// A topic node in the graph
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|
#[derive(Debug, Clone, Serialize, Deserialize)]
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|
pub struct TopicNode {
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/// OpenAlex topic ID
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|
pub id: String,
|
|
|
|
/// Topic display name
|
|
pub name: String,
|
|
|
|
/// Number of works in this topic
|
|
pub work_count: usize,
|
|
|
|
/// Average citation count
|
|
pub avg_citations: f64,
|
|
|
|
/// Growth rate (works per year)
|
|
pub growth_rate: f64,
|
|
}
|
|
|
|
/// An edge between topics
|
|
#[derive(Debug, Clone, Serialize, Deserialize)]
|
|
pub struct TopicEdge {
|
|
/// Source topic ID
|
|
pub source: String,
|
|
|
|
/// Target topic ID
|
|
pub target: String,
|
|
|
|
/// Number of citations across boundary
|
|
pub citation_count: usize,
|
|
|
|
/// Normalized weight
|
|
pub weight: f64,
|
|
}
|
|
|
|
impl TopicGraph {
|
|
/// Build topic graph from works
|
|
pub fn from_works(works: &[Work]) -> Self {
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|
let mut topics: HashMap<String, TopicNode> = HashMap::new();
|
|
let mut edge_counts: HashMap<(String, String), usize> = HashMap::new();
|
|
|
|
let mut min_date = Utc::now();
|
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let mut max_date = DateTime::<Utc>::MIN_UTC;
|
|
|
|
for work in works {
|
|
if let Some(date) = work.publication_date {
|
|
if date < min_date {
|
|
min_date = date;
|
|
}
|
|
if date > max_date {
|
|
max_date = date;
|
|
}
|
|
}
|
|
|
|
// Get work's primary topic
|
|
let source_topic = match &work.primary_topic {
|
|
Some(t) => t.id.clone(),
|
|
None => continue,
|
|
};
|
|
|
|
// Update or create topic node
|
|
let node = topics.entry(source_topic.clone()).or_insert_with(|| TopicNode {
|
|
id: source_topic.clone(),
|
|
name: work
|
|
.primary_topic
|
|
.as_ref()
|
|
.map(|t| t.display_name.clone())
|
|
.unwrap_or_default(),
|
|
work_count: 0,
|
|
avg_citations: 0.0,
|
|
growth_rate: 0.0,
|
|
});
|
|
node.work_count += 1;
|
|
node.avg_citations = (node.avg_citations * (node.work_count - 1) as f64
|
|
+ work.cited_by_count as f64)
|
|
/ node.work_count as f64;
|
|
|
|
// For simplicity, we'd need referenced works' topics
|
|
// This is a simplified model
|
|
}
|
|
|
|
// Calculate growth rates
|
|
let time_span_years = (max_date - min_date).num_days() as f64 / 365.0;
|
|
for node in topics.values_mut() {
|
|
node.growth_rate = if time_span_years > 0.0 {
|
|
node.work_count as f64 / time_span_years
|
|
} else {
|
|
0.0
|
|
};
|
|
}
|
|
|
|
// Build edges
|
|
let edges: Vec<TopicEdge> = edge_counts
|
|
.into_iter()
|
|
.map(|((src, tgt), count)| {
|
|
let src_count = topics.get(&src).map(|n| n.work_count).unwrap_or(1);
|
|
let tgt_count = topics.get(&tgt).map(|n| n.work_count).unwrap_or(1);
|
|
let weight = count as f64 / (src_count * tgt_count) as f64;
|
|
|
|
TopicEdge {
|
|
source: src,
|
|
target: tgt,
|
|
citation_count: count,
|
|
weight,
|
|
}
|
|
})
|
|
.collect();
|
|
|
|
Self {
|
|
topics,
|
|
edges,
|
|
time_window: (min_date, max_date),
|
|
}
|
|
}
|
|
|
|
/// Get number of topics
|
|
pub fn topic_count(&self) -> usize {
|
|
self.topics.len()
|
|
}
|
|
|
|
/// Get number of edges
|
|
pub fn edge_count(&self) -> usize {
|
|
self.edges.len()
|
|
}
|
|
|
|
/// Get topics by growth rate
|
|
pub fn fastest_growing(&self, top_k: usize) -> Vec<&TopicNode> {
|
|
let mut nodes: Vec<_> = self.topics.values().collect();
|
|
nodes.sort_by(|a, b| {
|
|
b.growth_rate
|
|
.partial_cmp(&a.growth_rate)
|
|
.unwrap_or(std::cmp::Ordering::Equal)
|
|
});
|
|
nodes.into_iter().take(top_k).collect()
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_entity_endpoints() {
|
|
assert_eq!(EntityType::Work.endpoint(), "works");
|
|
assert_eq!(EntityType::Author.endpoint(), "authors");
|
|
assert_eq!(EntityType::Topic.endpoint(), "topics");
|
|
}
|
|
|
|
#[test]
|
|
fn test_default_config() {
|
|
let config = OpenAlexConfig::default();
|
|
assert_eq!(config.base_url, "https://api.openalex.org");
|
|
assert!(config.use_cursor);
|
|
}
|
|
|
|
#[test]
|
|
fn test_source_with_filters() {
|
|
let config = OpenAlexConfig::default();
|
|
let source = OpenAlexSource::new(config)
|
|
.with_year_range(2020, 2024)
|
|
.open_access_only();
|
|
|
|
assert!(source.filters.contains_key("publication_year"));
|
|
assert!(source.filters.contains_key("open_access.is_oa"));
|
|
}
|
|
}
|