# LE_SC

**Repository Path**: WangJinLiang3/LE_SC

## Basic Information

- **Project Name**: LE_SC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-12
- **Last Updated**: 2025-12-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Spectral Methods for Directed Graph Clustering

This repository contains experiments and analysis for clustering **directed graphs** using three datasets:

- **US Migration** — migration data between US counties from 1995 to 2000.
- **Connectome** — neuron connectivity data from Larval Drosophila mushroom body connectome.
- **DSBM** — Directed Stochastic Block Model, a synthetic benchmark for evaluating clustering algorithms.

```
├── Connectome/                    # Neuron connectivity clustering
│   ├── adjacency.npz             # Adjacency matrix of neural network
│   ├── labels.npy                # Ground truth cluster labels
│   └── Neuron cluster.ipynb      # Notebook for clustering neuron data
│
├── DSBM/                         # DSBM clustering experiments
│   ├── Cluster DSBM_k2.ipynb     # Clustering DSBM with k=2
│
├── US migration/                 # US county migration clustering
│   ├── Coord.csv                 # Coordinates of US counties
│   ├── MIG.csv                   # Migration data between counties
│   ├── US_county_cluster.ipynb   # Notebook for clustering US counties
│   └── USA.txt                   # Supplementary US data
│
├── utils/                        # Utility scripts
│   ├── ClusterAlgorithms.py      # Clustering algorithm implementations
│   ├── RandomGraph.py            # Random graph generators
│   └── USA_Plot.py               # Plotting tools for US maps

```
## Key Algorithm

The file `utils/ClusterAlgorithms.py` includes multiple baseline methods for clustering directed graphs. Our proposed method, LE-SC (Likelihood Estimation Spectral Clustering), is an iterative spectral clustering algorithm that refines clusters through a likelihood-based update process to produce final community labels.

## Getting Started

```bash
# Clone the repo
git clone <repo-url>
cd <repo-folder>

# (Optional) create and activate virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt