Papers

Connected Papers is a great service, but it is too expensive, and uncustomizable. Here we implement a version with memgraph. The code is based on Python asyncio.

The service watches changes in the notes folder, which contains markdown files with YAML frontmatters. On file changes, it will try to update information about the paper.

• Meta data are read from markdown JSON front matter.
  • The front matter must contain the corpus_id field.
  • Optionally, it may contain a topics list of strings.
  • Optionally, it may contain a star field in 1, 2, 3, 4, 5.
• The file name and directory structure are ignored by the program.

Quick Start

1. Set up memgraph on localhost by docker compose up -d. Access the memgraph-lab for visualization.
   • Ensure that memgraph is in IN_MEMORY_ANALYTIC mode. Otherwise parallel transactions might have conflict, and we haven't implemented retry yet.
2. Install Python dependencies requests, neo4j, yaml
3. Run python watch.py to start the service.
4. Write CYPHER queries and visualize citations in Memgraph Lab. (There is no plan for a custom GUI yet.)
5. Either manually add note files, or run python fetch.py <arxivID / corpusID> <optional nick name>.

Database Schema

Node Types:

• Paper:
  • corpusId: int (key)
  • name: str
  • year: int
  • abstract: str
  • publicationDate: int
  • referenceCount: int
  • citationCount: int
  • stars: int
• URL:
  • name: str
  • url: str
• Author:
  • id: int
  • name: str
• Venue:
  • id: int
  • name: str
  • type: str
  • url: str
• Topic:
  • name: str

Relations:

• (Author)-[:AUTHORS]->(Paper)
  • rank: int in {0, 1, 2, ...}, 0 for the first author.
• (Paper)-[:JOINS]->(Venue)
• (Paper)-[:CITES]->(Paper)
• (Topic)-[:CONTAIN]->(Paper)

SemanticScholar API

This project is based on the S2 API. Read the documentation for more details. There is a Python wrapper of the S2 API here. This wrapper is absorbed by this repository.