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‎LICENSE‎

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‎experiments/README.md‎

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# SimKit Experiments with Neo4j & Python
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This repository demonstrates how to run spectral clustering experiments using both the SimKit plugin (loaded into Neo4j) and scikit-learn. It includes:
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- A Dockerfile to build a Neo4j container with the SimKit plugin.
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- The `experiments_2.py` script which runs experiments against the Neo4j instance.
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This repository demonstrates how to run spectral clustering experiments using both the SimKit plugin (loaded into Neo4j) and scikit-learn. It also includes experiments using Neo4j Graph Data Science (GDS) algorithms.
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## Features
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- Dockerized Neo4j setup with the SimKit plugin.
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- Scripts to compare clustering results using:
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- SimKit (custom Neo4j plugin)
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- scikit-learn
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- Neo4j GDS library
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- Batch and timing experiments for performance comparison.
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## Contents
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- Dockerfile and docker-compose.yml: Builds and runs a Neo4j instance with the SimKit plugin.
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- requirements.txt: Python dependencies.
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- SimKit-0.1.1.jar: SimKit plugin (must be compatible with the Neo4j version used).
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- Python scripts:
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- experiment_gds.py: Runs k-means clustering using Neo4j GDS.
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- experiments_simkit-0.1.1.py: Runs and times clustering using SimKit and scikit-learn.
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## Prerequisites
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- Docker must be installed on your system.
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- Docker installed on your system.
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- Python 3.x and pip installed.
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- Ensure your system has enough memory (Neo4j can be memory intensive).
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- Place the `simkit.jar` plugin file (compatible with your Neo4j version) in the same folder as the Dockerfile.
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## Building and Running the Neo4j Docker Container
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1. **Build the Docker Image**
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Open a terminal in the directory containing the Dockerfile and `simkit.jar` and run:
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```bash
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docker build -t my-neo4j .
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```
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This builds a Docker image named `my-neo4j` that includes the SimKit plugin.
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2. **Run the Docker Container**
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Start the container with:
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```bash
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docker run --name neo4j -p 7687:7687 -p 7474:7474 -d my-neo4j
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```
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- **Ports:**
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- `7687` is the Bolt port for Neo4j.
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- `7474` is the HTTP port (Neo4j Browser).
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3. **Verify the Container**
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- Check logs for any errors (especially plugin errors):
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```bash
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docker logs neo4j
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```
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- Open [http://localhost:7474](http://localhost:7474) in your browser to access the Neo4j Browser and verify that the SimKit procedures are available (e.g., try a test query like `RETURN simkit.experimental_spectralClustering({ ... })`).
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## Running the Experiments
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The `experiments_2.py` script runs spectral clustering experiments using both SimKit (via Neo4j procedures) and scikit-learn spectral clustering.
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1. **Install Python Dependencies**
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The script will attempt to install missing packages automatically. Alternatively, install manually:
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```bash
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pip install neo4j pandas psutil tqdm scikit-learn scipy
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```
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2. **Prepare Datasets**
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Place your dataset CSV files under the `datasets/` directory. The script expects file names such as `iris.csv`, `cora_nodes.csv`, `cora_edges.csv`, etc.
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3. **Run the Experiment Script**
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Ensure the Neo4j container is running, then execute:
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```bash
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python experiments_2.py
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```
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The script will:
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- Delete existing nodes and indexes in Neo4j.
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- Create feature/graph nodes from the datasets.
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- Run experiments using both SimKit and scikit-learn.
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- Save results as CSV files in a `results/` directory.
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- Memory: Ensure your system has enough RAM (Neo4j can be memory-intensive).
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- Place the simkit.jar plugin (compatible with your Neo4j version) in the same directory as the Dockerfile.
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## Troubleshooting
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## Setup Instructions
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1. Clone the repository:
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- **Container Exits Early:**
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Check the container logs with:
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```bash
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git clone https://github.com/yourusername/simkit-experiments.git
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cd simkit-experiments
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```
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```bash
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docker logs neo4j
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```
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2. Install Python dependencies:
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```bash
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pip install -r requirements.txt
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```
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Review for errors related to plugin incompatibility, configuration issues, or memory constraints.
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3. Start the Neo4j database:
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```bash
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docker compose up
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```
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- **SimKit Procedure Not Found:**
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Ensure the `simkit.jar` file is correctly placed and compatible with the Neo4j version used.
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This will build and start the Neo4j container with SimKit.
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- **Python Errors:**
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Verify that all dataset files exist and have the expected schema.
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## Cleanup
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4. Run experiments:
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- Run GDS clustering experiments on Neo4j:
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```bash
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python experiment_gds.py
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```
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To stop and remove the Docker container:
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- Run SimKit and scikit-learn timing experiments:
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```bash
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python experiments_simkit-0.1.1.py
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```
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```bash
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docker stop neo4j
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docker rm neo4j
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```
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## Notes
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- Ensure the Neo4j container is fully up before starting any experiments.
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- The scripts assume the Neo4j instance is accessible at the configured bolt:// address and uses default credentials (neo4j/neo4j by default; change if modified).
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- You may need to load or generate a sample graph dataset in Neo4j before running experiments.
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To remove the Docker image:
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## License
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```bash
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docker rmi my-neo4j
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```
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This project is licensed under the Apache License 2.0.

‎experiments/SimKit-0.1.1.jar‎

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‎experiments/docker-compose.yaml‎

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dbms.security.procedures.unrestricted: "simkit.*"
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volumes:
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# mount your locally built simkit.jar
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- ./simkit.jar:/var/lib/neo4j/plugins/simkit.jar
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- ./SimKit-0.1.1.jar:/var/lib/neo4j/plugins/simkit.jar
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- simkit_data:/data
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- simkit_logs:/logs
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ports:

experiments/experiment_gds_kmeans_0.1.1_batch.py renamed to experiments/experiment_gds.py

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