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.

Environment Setup

conda create --name <env> --file requirement.txt

Usage

Config:

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

dynamics: HindmarshRose, FitzHughNagumo or CoupledRossler

Run:

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}
}