# <img src="https://raw.githubusercontent.com/lszeremeta/knows/main/logo/knows-logo.png" alt="Knows logo" width="300">
[](https://pypi.org/project/knows/) [](https://hub.docker.com/r/lszeremeta/knows)
Knows is a powerful and user-friendly tool for benchmarking property graphs. These graphs are crucial in many fields.
Knows supports
multiple output formats and basic visualization capabilities, making it a go-to tool for researchers, educators 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 5.0](https://github.com/lszeremeta/yarspg), CSV, Cypher, GEXF, GML, JSON, and others.
- **Flexible Output Options**: Display results in the console, redirect them, or save them directly to a file.
- **Integrated Graph Visualization**: Conveniently visualize your graphs in SVG, PNG, JPG, or PDF 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.
- **Selectable Properties**: Choose which node and edge properties should be generated.
- **Reproducible graphs**: Ensure deterministic outputs by setting the `-s`/`--seed` option regardless of the selected
output format.
> **Note on reproducibility:** The `-s`/`--seed` option makes the random aspects of graph generation deterministic
> within the same software environment. Results may still differ across versions of Python or dependencies.
## 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 by default.
- Edges are labeled `knows` and include `strength` [1..100] and `lastMeetingDate` [1955-01-01..2025-06-28] properties by
default.
- Additional node properties:
- `favoriteColor`
- `company`
- `job`
- `phoneNumber`
- `postalAddress`
- `friendCount` [1..1000]
- `preferredContactMethod` [`inPerson`, `email`, `postalMail`, `phone`, `textMessage`, `videoCall`, `noPreference`]
- Additional edge properties:
- `lastMeetingCity`
- `meetingCount` [1..10000]
- Edges have random nodes, avoiding cycles.
- If edges connect the same nodes in both directions, the paired edges share `lastMeetingCity`, `lastMeetingDate`, and
`meetingCount` values.
## Installation 🛠️
You can install knows via PyPI, Docker or run it from the source code.
### Install via PyPI
1. **Installation**:
```shell
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**:
```shell
knows [options]
```
### Docker Deployment 🐳
#### From Docker Hub
1. **Pull Image**:
```shell
docker pull lszeremeta/knows
```
2. **Run Container**:
```shell
docker run --rm lszeremeta/knows [options]
```
#### Building from Source
1. **Build Image**:
```shell
docker build -t knows .
```
2. **Run Container**:
```shell
docker run --rm knows [options]
```
### Python from Source
1. **Clone Repository**:
```shell
git clone git@github.com:lszeremeta/knows.git
cd knows
```
2. **Install Requirements**:
```shell
pip install .[draw]
```
3. **Execute Knows**:
```shell
python -m knows [options]
```
### Install Tkinter for Graph Visualization
The `-d`/`--draw` option requires Tkinter.
- **Ubuntu**:
```shell
sudo apt update
sudo apt install python3-tk
```
See [Installing Tkinter on Ubuntu](https://www.pythonguis.com/installation/install-tkinter-linux/) for details.
- **macOS (Homebrew)**:
```shell
brew install python3
brew install python-tk
```
See [Installing Tkinter on macOS](https://www.pythonguis.com/installation/install-tkinter-mac/) for details.
- **Windows**:
On Windows, Tkinter should be installed by default with Python. No additional steps required.
## Usage 💡
### Basic Usage
```shell
knows [-h] [-n NODES] [-e EDGES] [-s SEED] [-f {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}]
[-np [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...]]
[-ep [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]] [-ap] [-d]
[output]
```
> Available options may vary depending on the version. To display all available options with their descriptions use
`knows -h`.
### Positional arguments
- `output`: Optional path to save the graph. For CSV format two files will be created: `*_nodes.csv` and `*_edges.csv`.
### Options
- `-h`, `--help`: Show this help message and exit.
- `-n NODES`, `--nodes NODES`: Number of nodes in the graph. Selected randomly if not specified.
- `-e EDGES`, `--edges EDGES`: Number of edges in the graph. Selected randomly if not specified.
- `-s SEED`, `--seed SEED`: Seed for random number generation to ensure reproducible results (also between various
output formats).
- `-f {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}, --format {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}`:
Format to output the graph. Default: `yarspg`. The `svg`, `png`, `jpg` and `pdf` formats are for simple graph
visualization.
- `-np [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...], --node-props [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...]`:
Space-separated node properties. Available: `firstName`, `lastName`, `company`, `job`, `phoneNumber`, `favoriteColor`,
`postalAddress`, `friendCount`, `preferredContactMethod`.
- `-ep [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]`,
`--edge-props [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]`:
Space-separated edge properties. Available: `strength`, `lastMeetingCity`, `lastMeetingDate`, `meetingCount`.
- `-ap`, `--all-props`: Use all available node and edge properties.
- `-d`, `--draw`: Show simple image of the graph. Requires Tkinter. This option
may not work in Docker. If you want to generate an image of the graph, use the `svg`, `png`, `jpg`, or `pdf` output
format and save it to a file.
### Practical Examples 🌟
1. Create a random graph in [YARS-PG 5.0 format](https://github.com/lszeremeta/yarspg) and show it:
```shell
knows
# or
docker run --rm lszeremeta/knows
```
2. Create a 100-node, 70-edge graph in GraphML format:
```shell
knows -n 100 -e 70 -f graphml > graph.graphml
# or
knows -n 100 -e 70 -f graphml graph.graphml
# or
docker run --rm lszeremeta/knows -n 100 -e 70 -f graphml > graph.graphml
# or
docker run --rm -v "$(pwd)":/data lszeremeta/knows -n 100 -e 70 -f graphml /data/graph.graphml
```
3. Create a random graph in CSV format and save to files (nodes are written to standard output, edges to standard
error):
```shell
knows -f csv > nodes.csv 2> edges.csv
# or
knows -f csv graph.csv
# or
docker run --rm lszeremeta/knows -f csv > nodes.csv 2> edges.csv
# or
docker run --rm -v "$(pwd)":/data lszeremeta/knows -f csv /data/graph.csv
```
The latter command creates `graph_nodes.csv` and `graph_edges.csv`.
4. Create a 50-node, 20-edge graph in Cypher format:
```shell
knows -n 50 -e 20 -f cypher > graph.cypher
# or
knows -n 50 -e 20 -f cypher graph.cypher
```
5. Create a 100-node, 50-edge graph in YARS-PG format:
```shell
knows -n 100 -e 50 > graph.yarspg
# or
knows -n 100 -e 50 graph.yarspg
```
6. Create, save, and visualize a 100-node, 50-edge graph in SVG:
```shell
knows -n 100 -e 50 -f svg -d > graph.svg
# or
knows -n 100 -e 50 -f svg -d graph.svg
```
7. Create, save a 70-node, 50-edge graph in SVG:
```shell
knows -n 70 -e 50 -f svg > graph.svg
# or
knows -n 70 -e 50 -f svg graph.svg
```
8. Create, save a 10-node, 5-edge graph in PNG:
```shell
knows -n 10 -e 5 -f png > graph.png
# or
knows -n 10 -e 5 -f png graph.png
```
9. Create a graph in JSON format:
```shell
knows -f json > graph.json
# or
knows -f json graph.json
```
10. Create a graph with custom properties (20 nodes, 10 edges) and show it:
```shell
knows -n 20 -e 10 -np firstName favoriteColor job -ep lastMeetingCity
```
11. Create a graph with all possible properties in YARS-PG format and save it to file:
```shell
knows -ap > graph.yarspg
# or
knows -ap graph.yarspg
```
12. Generate a reproducible graph in CSV by setting a seed:
```shell
knows -n 3 -e 2 -s 43 -f csv
```
Running the command again with the same seed will produce the identical graph, provided the environment and dependencies
remain unchanged.
13. Generate the same graph as above but in YARS-PG format:
```shell
knows -n 3 -e 2 -s 43
```
## 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](https://opensource.guide/how-to-contribute/)
and [CONTRIBUTING.md](https://github.com/lszeremeta/knows/blob/main/CONTRIBUTING.md).
## License 📜
Knows is licensed under the [MIT License](https://github.com/lszeremeta/knows/blob/main/LICENSE).
Raw data
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"author_email": "\u0141ukasz Szeremeta <l.szeremeta.dev+knows@gmail.com>",
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"description": "# <img src=\"https://raw.githubusercontent.com/lszeremeta/knows/main/logo/knows-logo.png\" alt=\"Knows logo\" width=\"300\">\n\n[](https://pypi.org/project/knows/) [](https://hub.docker.com/r/lszeremeta/knows)\n\nKnows is a powerful and user-friendly tool for benchmarking property graphs. These graphs are crucial in many fields.\nKnows supports\nmultiple output formats and basic visualization capabilities, making it a go-to tool for researchers, educators and data\nenthusiasts.\n\n## Key Features \ud83d\ude80\n\n- **Customizable Graph Generation**: Tailor your graphs by specifying the number of nodes and edges.\n- **Diverse Output Formats**: Export graphs in formats like\n GraphML, [YARS-PG 5.0](https://github.com/lszeremeta/yarspg), CSV, Cypher, GEXF, GML, JSON, and others.\n- **Flexible Output Options**: Display results in the console, redirect them, or save them directly to a file.\n- **Integrated Graph Visualization**: Conveniently visualize your graphs in SVG, PNG, JPG, or PDF format.\n- **Intuitive Command-Line Interface (CLI)**: A user-friendly CLI for streamlined graph generation and visualization.\n- **Docker Compatibility**: Deploy Knows in Docker containers for a consistent and isolated runtime environment.\n- **Selectable Properties**: Choose which node and edge properties should be generated.\n- **Reproducible graphs**: Ensure deterministic outputs by setting the `-s`/`--seed` option regardless of the selected\n output format.\n\n> **Note on reproducibility:** The `-s`/`--seed` option makes the random aspects of graph generation deterministic\n> within the same software environment. Results may still differ across versions of Python or dependencies.\n\n## Graph Structure\n\n- Generates graphs with specified or random nodes and edges.\n- Creates directed graphs.\n- Nodes are labeled `Person` with unique IDs (`N1, N2, N3, ..., Nn`).\n- Nodes feature `firstName` and `lastName` properties by default.\n- Edges are labeled `knows` and include `strength` [1..100] and `lastMeetingDate` [1955-01-01..2025-06-28] properties by\n default.\n- Additional node properties:\n - `favoriteColor`\n - `company`\n - `job`\n - `phoneNumber`\n - `postalAddress`\n - `friendCount` [1..1000]\n - `preferredContactMethod` [`inPerson`, `email`, `postalMail`, `phone`, `textMessage`, `videoCall`, `noPreference`]\n- Additional edge properties:\n - `lastMeetingCity`\n - `meetingCount` [1..10000]\n- Edges have random nodes, avoiding cycles.\n- If edges connect the same nodes in both directions, the paired edges share `lastMeetingCity`, `lastMeetingDate`, and\n `meetingCount` values.\n\n## Installation \ud83d\udee0\ufe0f\n\nYou can install knows via PyPI, Docker or run it from the source code.\n\n### Install via PyPI\n\n1. **Installation**:\n ```shell\n pip install knows[draw]\n ```\n The `draw` installs a `matplotlib` library for graph visualization. You can omit the `[draw]` if you don't need\n visualization and `svg` output generation.\n\n2. **Running Knows**:\n ```shell\n knows [options]\n ```\n\n### Docker Deployment \ud83d\udc33\n\n#### From Docker Hub\n\n1. **Pull Image**:\n ```shell\n docker pull lszeremeta/knows\n ```\n\n2. **Run Container**:\n ```shell\n docker run --rm lszeremeta/knows [options]\n ```\n\n#### Building from Source\n\n1. **Build Image**:\n ```shell\n docker build -t knows .\n ```\n\n2. **Run Container**:\n ```shell\n docker run --rm knows [options]\n ```\n\n### Python from Source\n\n1. **Clone Repository**:\n ```shell\n git clone git@github.com:lszeremeta/knows.git\n cd knows\n ```\n\n2. **Install Requirements**:\n ```shell\n pip install .[draw]\n ```\n\n3. **Execute Knows**:\n ```shell\n python -m knows [options]\n ```\n\n### Install Tkinter for Graph Visualization\n\nThe `-d`/`--draw` option requires Tkinter.\n\n- **Ubuntu**:\n ```shell\n sudo apt update\n sudo apt install python3-tk\n ```\n See [Installing Tkinter on Ubuntu](https://www.pythonguis.com/installation/install-tkinter-linux/) for details.\n\n- **macOS (Homebrew)**:\n ```shell\n brew install python3\n brew install python-tk\n ```\n See [Installing Tkinter on macOS](https://www.pythonguis.com/installation/install-tkinter-mac/) for details.\n\n- **Windows**:\n On Windows, Tkinter should be installed by default with Python. No additional steps required.\n\n## Usage \ud83d\udca1\n\n### Basic Usage\n\n```shell\nknows [-h] [-n NODES] [-e EDGES] [-s SEED] [-f {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}]\n [-np [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...]]\n [-ep [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]] [-ap] [-d]\n [output]\n```\n\n> Available options may vary depending on the version. To display all available options with their descriptions use\n`knows -h`.\n\n### Positional arguments\n\n- `output`: Optional path to save the graph. For CSV format two files will be created: `*_nodes.csv` and `*_edges.csv`.\n\n### Options\n\n- `-h`, `--help`: Show this help message and exit.\n- `-n NODES`, `--nodes NODES`: Number of nodes in the graph. Selected randomly if not specified.\n- `-e EDGES`, `--edges EDGES`: Number of edges in the graph. Selected randomly if not specified.\n- `-s SEED`, `--seed SEED`: Seed for random number generation to ensure reproducible results (also between various\n output formats).\n- `-f {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}, --format {yarspg,graphml,csv,cypher,gexf,gml,svg,png,jpg,pdf,adjacency_list,multiline_adjacency_list,edge_list,json}`:\nFormat to output the graph. Default: `yarspg`. The `svg`, `png`, `jpg` and `pdf` formats are for simple graph\nvisualization.\n- `-np [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...], --node-props [{firstName,lastName,company,job,phoneNumber,favoriteColor,postalAddress,friendCount,preferredContactMethod} ...]`: \nSpace-separated node properties. Available: `firstName`, `lastName`, `company`, `job`, `phoneNumber`, `favoriteColor`,\n`postalAddress`, `friendCount`, `preferredContactMethod`.\n- `-ep [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]`, \n `--edge-props [{strength,lastMeetingCity,lastMeetingDate,meetingCount} ...]`: \n Space-separated edge properties. Available: `strength`, `lastMeetingCity`, `lastMeetingDate`, `meetingCount`.\n- `-ap`, `--all-props`: Use all available node and edge properties.\n- `-d`, `--draw`: Show simple image of the graph. Requires Tkinter. This option\n may not work in Docker. If you want to generate an image of the graph, use the `svg`, `png`, `jpg`, or `pdf` output\n format and save it to a file.\n\n### Practical Examples \ud83c\udf1f\n\n1. Create a random graph in [YARS-PG 5.0 format](https://github.com/lszeremeta/yarspg) and show it:\n ```shell\n knows\n # or\n docker run --rm lszeremeta/knows\n ```\n2. Create a 100-node, 70-edge graph in GraphML format:\n ```shell\n knows -n 100 -e 70 -f graphml > graph.graphml\n # or\n knows -n 100 -e 70 -f graphml graph.graphml\n # or\n docker run --rm lszeremeta/knows -n 100 -e 70 -f graphml > graph.graphml\n # or\n docker run --rm -v \"$(pwd)\":/data lszeremeta/knows -n 100 -e 70 -f graphml /data/graph.graphml\n ```\n3. Create a random graph in CSV format and save to files (nodes are written to standard output, edges to standard\n error):\n ```shell\n knows -f csv > nodes.csv 2> edges.csv\n # or\n knows -f csv graph.csv\n # or\n docker run --rm lszeremeta/knows -f csv > nodes.csv 2> edges.csv\n # or\n docker run --rm -v \"$(pwd)\":/data lszeremeta/knows -f csv /data/graph.csv\n ```\n The latter command creates `graph_nodes.csv` and `graph_edges.csv`.\n4. Create a 50-node, 20-edge graph in Cypher format:\n ```shell\n knows -n 50 -e 20 -f cypher > graph.cypher\n # or\n knows -n 50 -e 20 -f cypher graph.cypher\n ```\n5. Create a 100-node, 50-edge graph in YARS-PG format:\n ```shell\n knows -n 100 -e 50 > graph.yarspg\n # or\n knows -n 100 -e 50 graph.yarspg\n ```\n6. Create, save, and visualize a 100-node, 50-edge graph in SVG:\n ```shell\n knows -n 100 -e 50 -f svg -d > graph.svg\n # or\n knows -n 100 -e 50 -f svg -d graph.svg\n ```\n7. Create, save a 70-node, 50-edge graph in SVG:\n ```shell\n knows -n 70 -e 50 -f svg > graph.svg\n # or\n knows -n 70 -e 50 -f svg graph.svg\n ```\n8. Create, save a 10-node, 5-edge graph in PNG:\n ```shell\n knows -n 10 -e 5 -f png > graph.png\n # or\n knows -n 10 -e 5 -f png graph.png\n ```\n9. Create a graph in JSON format:\n ```shell\n knows -f json > graph.json\n # or\n knows -f json graph.json\n ```\n10. Create a graph with custom properties (20 nodes, 10 edges) and show it:\n ```shell\n knows -n 20 -e 10 -np firstName favoriteColor job -ep lastMeetingCity\n ```\n11. Create a graph with all possible properties in YARS-PG format and save it to file:\n ```shell\n knows -ap > graph.yarspg\n # or\n knows -ap graph.yarspg\n ```\n12. Generate a reproducible graph in CSV by setting a seed:\n ```shell\n knows -n 3 -e 2 -s 43 -f csv\n ```\n\nRunning the command again with the same seed will produce the identical graph, provided the environment and dependencies\nremain unchanged.\n\n13. Generate the same graph as above but in YARS-PG format:\n\n ```shell\n knows -n 3 -e 2 -s 43\n ```\n\n## Contribute to Knows \ud83d\udc65\n\nYour ideas and contributions can make Knows even better! If you're new to open source,\nread [How to Contribute to Open Source](https://opensource.guide/how-to-contribute/)\nand [CONTRIBUTING.md](https://github.com/lszeremeta/knows/blob/main/CONTRIBUTING.md).\n\n## License \ud83d\udcdc\n\nKnows is licensed under the [MIT License](https://github.com/lszeremeta/knows/blob/main/LICENSE).\n",
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