paper-weaver 1.1.1

Weave academic paper data from various sources (DBLP, Semantic Scholar) into graph databases.

PaperWeaver

PaperWeaver is a tool for weaving academic paper data from various sources (DBLP, Semantic Scholar) into graph databases (Neo4j). It uses BFS traversal to explore and collect papers, authors, venues, citations, and references, building a comprehensive academic knowledge graph.

Features

• Multiple Data Sources

  • DBLP API - bibliographic information
  • Semantic Scholar API - citations and references

• Graph Database Output

  • Neo4j - store papers, authors, venues and their relationships

• Flexible Caching

  • In-memory cache for simple use cases
  • Redis cache for distributed and persistent caching

• BFS Traversal

  • Start from authors, papers, or venues
  • Automatically discover related entities through citations, references, and authorship

Installation

pip install paper-weaver

Or install from source:

git clone https://github.com/yindaheng98/PaperWeaver.git
cd PaperWeaver
pip install -e .

Quick Start

Basic Usage

Start from an author and explore their papers and related venues:

paper-weaver \
  --init-mode authors \
  --init-dblp-pids h/KaimingHe \
  --datadst-neo4j-uri bolt://localhost:7687 \
  --datadst-neo4j-user neo4j \
  --datadst-neo4j-password your-password \
  -n 10 -v

Start from Papers

paper-weaver \
  --init-mode papers \
  --init-dblp-record-keys conf/cvpr/HeZRS16 journals/pami/HeZRS16 \
  --datadst-neo4j-uri bolt://localhost:7687 \
  -n 5 -v

Start from Venues

paper-weaver \
  --init-mode venues \
  --init-dblp-venue-keys db/conf/cvpr/cvpr2016 \
  --datadst-neo4j-uri bolt://localhost:7687 \
  -n 5 -v

Command-Line Options

Weaver Options

Option	Default	Description
--weaver-type	a2p2v	Weaver type
-n, --max-iterations	0	Max BFS iterations (0 = until no new data)
-v, --verbose	-	Increase verbosity (-v: INFO, -vv: DEBUG)

Initialization Options

Option	Default	Description
--init-type	dblp	Initializer type
--init-mode	authors	Initialization mode: papers, authors, or venues
--init-dblp-record-keys	-	DBLP record keys (e.g., conf/cvpr/HeZRS16)
--init-dblp-pids	-	DBLP person IDs (e.g., h/KaimingHe)
--init-dblp-venue-keys	-	DBLP venue keys (e.g., db/conf/cvpr/cvpr2016)

Data Source Options

Option	Default	Description
--datasrc-type	dblp	Data source: dblp or semanticscholar
--datasrc-cache-mode	memory	Cache backend: memory or redis
--datasrc-redis-url	redis://localhost:6379	Redis URL for data source cache
--datasrc-max-concurrent	10	Maximum concurrent HTTP requests
--datasrc-http-proxy	-	HTTP proxy URL
--datasrc-http-timeout	30	HTTP timeout in seconds
--datasrc-ss-api-key	-	Semantic Scholar API key

Cache Options

Option	Default	Description
--cache-mode	memory	Cache backend: memory or redis
--cache-redis-url	redis://localhost:6379	Default Redis URL
--cache-redis-prefix	paper-weaver-cache	Redis key prefix

Neo4j Options

Option	Default	Description
--datadst-neo4j-uri	bolt://localhost:7687	Neo4j connection URI
--datadst-neo4j-user	neo4j	Neo4j username
--datadst-neo4j-password	neo4j	Neo4j password
--datadst-neo4j-database	neo4j	Neo4j database name

Using with Redis Cache

For large-scale crawling, use Redis for persistent caching:

paper-weaver \
  --init-mode authors \
  --init-dblp-pids h/KaimingHe \
  --cache-mode redis \
  --cache-redis-url redis://localhost:6379 \
  --datasrc-cache-mode redis \
  --datasrc-redis-url redis://localhost:6379 \
  --datadst-neo4j-uri bolt://localhost:7687 \
  -v

Graph Schema

PaperWeaver creates the following nodes and relationships in Neo4j:

Nodes

• Paper: title, year, venue, doi, etc.
• Author: name, pid, orcid, etc.
• Venue: name, type (journal/proceedings/book)

Relationships

• (Author)-[:AUTHORED]->(Paper)
• (Paper)-[:PUBLISHED_IN]->(Venue)
• (Paper)-[:CITES]->(Paper)
• (Paper)-[:REFERENCES]->(Paper)

Python API

import asyncio
from paper_weaver import Author2Paper2VenueWeaver
from paper_weaver.datasrc.dblp import DBLPDataSrc
from paper_weaver.datadst.neo4j import Neo4jDataDst
from paper_weaver.cache import HybridCacheBuilder
from paper_weaver.initializer.dblp import DBLPAuthorsInitializer

async def main():
    # Setup components
    datasrc = DBLPDataSrc()
    cache = HybridCacheBuilder().with_all_memory().build_weaver_cache()
    initializer = DBLPAuthorsInitializer(["h/KaimingHe"])
    
    # Setup Neo4j
    from neo4j import AsyncGraphDatabase
    driver = AsyncGraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
    session = driver.session(database="neo4j")
    datadst = Neo4jDataDst(session)
    
    # Create weaver and run
    weaver = Author2Paper2VenueWeaver(
        src=datasrc,
        dst=datadst,
        cache=cache,
        initializer=initializer
    )
    
    total = await weaver.bfs(max_iterations=10)
    print(f"Processed {total} items")
    
    await driver.close()

asyncio.run(main())

Requirements

• Python 3.10+
• Neo4j 4.0+ (for graph storage)
• Redis (optional, for distributed caching)

License

MIT License