knows

Property graph benchmark that creates graphs with specified node and edge numbers, supporting multiple output formats and visualization


Keywords
knows, benchmark, yarspg, graphs, mathematics, CLI, GraphML, YARS-PG, property, graph, database, data, Neo4j, NetworkX, Faker, analysis, benchmarking, visualization, science, algorithms, network, tool, analytics, educational, research, generation, python, docker, command, line, interface, json, svg, generator, graphml-generator, property-graphs
License
MIT
Install
pip install knows==1.0.0

Documentation

Knows logo

PyPI Docker Image Size (latest by date)

Knows is a user-friendly tool for benchmarking property graphs. These graphs are crucial in many fields. Knows supports multiple output formats and visualization capabilities, making it a go-to tool for educators, researchers, and data enthusiasts.

Key Features 🚀

  • Customizable Graph Generation: Tailor your graphs by specifying the number of nodes and edges.
  • Diverse Output Formats: Export graphs in formats like GraphML, YARS-PG, GEXF, GML, SVG, JSON, and others.
  • Integrated Graph Visualization: Conveniently visualize your graphs in SVG format.
  • Intuitive Command-Line Interface (CLI): A user-friendly CLI for streamlined graph generation and visualization.
  • Docker Compatibility: Deploy Knows in Docker containers for a consistent and isolated runtime environment.

Graph Structure

  • Generates graphs with specified or random nodes and edges.
  • Creates directed graphs.
  • Nodes are labeled Person with unique IDs (N1, N2, N3, ..., Nn).
  • Nodes feature firstName and lastName properties with randomly assigned names.
  • Edges are labeled knows and include a createDate property with a random date.
  • Edges have random nodes, avoiding cycles.

Installation 🛠️

You can install knows via PyPI Docker or run it from the source code.

Install via PyPI

  1. Installation:

    pip install knows[draw]

    The draw installs a matplotlib library for graph visualization. You can omit the [draw] if you don't need visualization and svg output generation.

  2. Running Knows:

    knows [nodes] [edges] [options]

Docker Deployment 🐳

From Docker Hub

  1. Pull Image:

    docker pull lszeremeta/knows
  2. Run Container:

    docker run --rm lszeremeta/knows [nodes] [edges] [options]

Building from Source

  1. Build Image:

    docker build -t knows .
  2. Run Container:

    docker run --rm knows [nodes] [edges] [options]

Python from Source

  1. Clone Repository:

    git clone git@github.com:lszeremeta/knows.git
    cd knows
  2. Install Requirements:

    pip install -r requirements.txt
  3. Execute Knows:

    python -m knows [nodes] [edges] [options]

Usage 💡

Basic Usage

knows [nodes] [edges] [options]

To view all available options, use:

knows -h

Positional Arguments

  1. nodes: Specify the number of nodes in the graph. Selected randomly if not specified.
  2. edges: Specify the number of edges in the graph. Selected randomly if not specified.

Options

  • -h, --help: Display the help message and exit the program.
  • -f {graphml,yarspg,gexf,gml,svg,adjacency_list,multiline_adjacency_list,edge_list,json}, --format {graphml,yarspg,gexf,gml,svg,adjacency_list,multiline_adjacency_list,edge_list,json}: Choose the format to output the graph. Default: graphml.
  • -d, --draw: Generate an image of the graph (default is no image). This option may not work in the Docker.

Practical Examples 🌟

  1. Create a random graph in GraphML format:
    knows
  2. Create a 100-node, 70-edge graph in YARS-PG format:
    knows 100 70 -f yarspg > graph.yarspg
  3. Create a 100-node, 50-edge graph in GraphML format:
     knows 100 50 > graph.graphml
  4. Create, save, and visualize a 100-node, 50-edge graph in SVG:
    knows 100 50 -f svg -d > graph.svg
  5. Create, save a 100-node, 50-edge graph in SVG with a custom filename:
     knows 100 50 -f svg > graph.svg
  6. Create a graph in JSON format:
    knows -f json > graph.json

Contribute to Knows 👥

Your ideas and contributions can make Knows even better! If you're new to open source, read How to Contribute to Open Source and CONTRIBUTING.md.

License 📜

Knows is licensed under the MIT License.