<h1 align="center"> Orbicon </h1> <h2 align="center"> 🔍 Explore Your Community 🧑 </h2> <p align="center"> <a href="https://github.com/memgraph/orbicon/blob/main/LICENSE"> <img src="https://img.shields.io/github/license/memgraph/orbicon" alt="license" title="license"/> </a> <a href="https://github.com/memgraph/orbicon"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" alt="build" title="build"/> </a> <a href="#contributors-"> <img src="https://img.shields.io/badge/all_contributors-3-green.svg?style=flat" /> </a> </p> <p align="center"> <a href="https://twitter.com/intent/follow?screen_name=memgraphdb"><img src="https://img.shields.io/twitter/follow/memgraphdb.svg?label=Follow%20@memgraphdb" alt="Follow @memgraphdb" /></a> </p> <p align="center"> <a href="https://github.com/memgraph/orbicon"> <img src="https://public-assets.memgraph.com/github-readme-images/orbicon-01.png" alt="orbit_and_memgraph" title="orbit_and_memgraph" style="width: 80%"/> </a> </p>

Orbicon is a centralized place where you can find anything about your developer community. Orbit aggregates all sorts of different events coming from the community, while Memgraph enriches the data and provides advanced insights based on incremental graph algorithms.

Orbit provides the Love metric, which indicates how much a given community member loves your brand. In addition to data coming from Orbit, Memgraph scrapes social graph data from Github and Twitter. Based on these social networks, Memgraph constructs an entirely new membership graph. Memgraph then analyzes this membership graph by applying the following graph algorithms:

PageRank: This algorithm tells us how important each member is.
Community Detection: This algorithm reveals deeper insights about the network structure and possible sub-communities.

📚 Data model

👉 Try it out!

The demo application - orbit.memgraph.com
The Memgraph instance - bolt://orbit.memgraph.com:7687

To explore the data, please download Memgraph Lab. The endpoint is orbit.memgraph.com and the port is 7687.

🖥️ Run the app locally

Running the backend service

The first thing to do in the root directory is to create a Python virtual environment:

python3 -m venv .venv
source .venv/bin/activate

After that, install all the dependencies with Poetry:

cd backend
poetry install

Finally, start the backend service with:

python3 main.py

Running the frontend service

Position yourself in the frontend directory, install the dependencies with npm and run using the following commands:

cd frontend
npm install
npm run serve

❔ Find out more about Memgraph

Memgraph makes creating real-time streaming graph applications accessible to every developer. Spin up an instance, consume data directly from Kafka, and build on top of everything from super-fast graph queries to PageRank and Community Detection.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

<table> <tr> <td align="center"><a href="https://github.com/Josipmrden"><img src="https://avatars.githubusercontent.com/u/22621791?v=4" width="100px;" alt=""/><br /><sub><b>Josip Mrden</b></sub></a></td> <td align="center"><a href="https://github.com/gitbuda"><img src="https://avatars.githubusercontent.com/u/4950251?v=4" width="100px;" alt=""/><br /><sub><b>Marko Budiselic</b></sub></a></td> <td align="center"><a href="https://github.com/antoniofilipovic"><img src="https://avatars.githubusercontent.com/u/61245998?v=4" width="100px;" alt=""/><br /><sub><b>Antonio Filipovic </b></sub></a></td> </tr> </table>

This project follows the all-contributors specification. Contributions of any kind welcome!

Orbicon

Install / Use

README