# Community Detection in Large-Scale Complex Networks via Structural Entropy Game (CoDeSEG)
CoDeSEG supports undirected, directed, weighted, unweighted, overlapping, non-overlapping, and dynamic community detection. The relevant parameter descriptions are as follows:
| Parameter | Description | Type | Default | Required |
|----------------|------------------------------------------------|---------|----------|----------|
| `in_path` | Input file of graph edge list | file | None | Yes |
| `out_path` | Output file of communities | file | None | Yes |
| `ground_truth` | Ground truth file | file | No | No |
| `weighted` | Weighted graph | bool | false | No |
| `directed` | directed graph | bool | false | No |
| `dynamic` | dynamic graph | bool | false | No |
| `overlap` | Overlapping communities | bool | false | No |
| `gamma` | Overlapping detecting factor | float | 1.0 | No |
| `tau` | Non-overlapping entropy threshold | float | 0.3 | No |
| `r` | Stable round threshold for dynamic detection | int | 2 | No |
| `it` | Maximum number of iterations | int | 10 | No |
| `parallel` | Number of threads | int | 1 | No |
| `verbose` | Print detection iteration messages | bool | false | No |
## Note
### Build Requirements
- CMake >= 3.22
- python >= 3.8
### data format
The format of the input edge list is as follows:
```text
1 \t 2 \n
1 \t 3 \n
2 \t 3 \n
```
For dynamic graphs, the input should be a file containing edge lists of multiple network snapshots, stored in the `/data/ntwk` directory. The file structure is as follows:
```text
data(your dataset)/
├── ntwk
├── 1.txt
├── 2.txt
├── 3.txt
...
```
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"description": "\n# Community Detection in Large-Scale Complex Networks via Structural Entropy Game (CoDeSEG)\n\nCoDeSEG supports undirected, directed, weighted, unweighted, overlapping, non-overlapping, and dynamic community detection. The relevant parameter descriptions are as follows:\n\n| Parameter | Description | Type | Default | Required |\n|----------------|------------------------------------------------|---------|----------|----------|\n| `in_path` | Input file of graph edge list | file | None | Yes |\n| `out_path` | Output file of communities | file | None | Yes |\n| `ground_truth` | Ground truth file | file | No | No |\n| `weighted` | Weighted graph | bool | false | No |\n| `directed` | directed graph | bool | false | No |\n| `dynamic` | dynamic graph | bool | false | No |\n| `overlap` | Overlapping communities | bool | false | No |\n| `gamma` | Overlapping detecting factor | float | 1.0 | No |\n| `tau` | Non-overlapping entropy threshold | float | 0.3 | No |\n| `r` | Stable round threshold for dynamic detection | int | 2 | No |\n| `it` | Maximum number of iterations | int | 10 | No |\n| `parallel` | Number of threads | int | 1 | No |\n| `verbose` | Print detection iteration messages | bool | false | No |\n\n\n## Note\n### Build Requirements\n- CMake >= 3.22\n- python >= 3.8\n\n### data format\nThe format of the input edge list is as follows:\n```text\n 1 \\t 2 \\n\n 1 \\t 3 \\n\n 2 \\t 3 \\n\n``` \nFor dynamic graphs, the input should be a file containing edge lists of multiple network snapshots, stored in the `/data/ntwk` directory. The file structure is as follows:\n```text\ndata(your dataset)/ \n\u251c\u2500\u2500 ntwk \n \u251c\u2500\u2500 1.txt\n \u251c\u2500\u2500 2.txt\n \u251c\u2500\u2500 3.txt\n ...\n``` \n",
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