Graph implementation that loads graph data (nodes and edges) from external sources and caches the loaded data in a database using sqlalchemy or flask-sqlalchemy.
Project description
GraphScraper is a Python 3 library that contains a base graph implementation designed to be turned into a web scraper for graph data. It has two major features:
1) The graph automatically manages a database (using either SQLAlchemy or Flask-SQLAlchemy) where it stores all the nodes and edges the graph has seen.
2) The base graph implementation provides hook methods that, if implemented, turn the graph into a web scraper.
Yet another graph implementation - why
There are many excellent graph libraries available for different purposes. I started implementing this one because i haven’t found a graph library that is dynamic (i don’t need the whole graph in memory - or on disk - before i start working with it), that can be used as a web scraper (to seamlessly load nodes and edges from some remote data source when that piece of data is needed) and that keeps all data (the graph) automatically up-to-date on the disk. GraphScraper aims to satisfy these requirements.
Demo - igraph
Besides the base graph implementation, a working demo using the igraph library is also included that shows how you can implement and use an actual graph-scraper. Instead of web-scraping, this demo uses an igraph graph instance as the “remote” source to scrape data from.
Dependencies
The project requires SQLAlchemy or Flask-SQLAlchemy to be installed. If you wish to the included igraph-based graph implementation, you will also need igraph library.
Contribution
Any form of constructive contribution (feedback, features, bug fixes, tests, additional documentation, etc.) is welcome.
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Source Distributions
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