https://github.com/tsinghua-fib-lab/DiskNet

Official implementation for "Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space" (KDD2024)
https://github.com/tsinghua-fib-lab/DiskNet

Last synced: 2 months ago
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Official implementation for "Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space" (KDD2024)

• Host: GitHub
• URL: https://github.com/tsinghua-fib-lab/DiskNet
• Owner: tsinghua-fib-lab
• License: mit
• Created: 2024-05-31T02:24:46.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-09-05T02:33:20.000Z (5 months ago)
• Last Synced: 2024-09-06T08:54:50.451Z (5 months ago)
• Language: Python
• Size: 353 KB
• Stars: 12
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DiskNet

The repo is the official implementation for our  paper: "Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space” (KDD 2024). 

## Overall Architecture

DiskNet: (1) Hyperbolic Renormalization Group, which identifies the representation and skeleton of network dynamics; (2) Neural Dynamics on Skeleton, which models the dynamics of super-nodes on the skeleton; and (3) Degree-based Super-Resolution, which lifts the predicted values of super-nodes to the original nodes.

![architecture](./asset/architecture.png)

## Environment Setup

```

conda create --name  --file requirement.txt

```

## Usage

**Config:**

graph_type: `BA `, `WS`, `Drosophila`, `Social`, `Web`, `PowerGrid` or `Airport`;

dynamics: `HindmarshRose`, `FitzHughNagumo` or `CoupledRossler`

**Run:**

```shell

python main.py

```

## Citation

If you find this repo helpful, please cite our paper.

```

@inproceedings{li2024predicting,

  title={Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space},

  author={Li, Ruikun and Wang, Huandong and Piao, Jinghua and Liao, Qingmin and Li, Yong},

  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},

  pages={1655--1666},

  year={2024}

}

```