首页|Predicting Long-term Dynamics of Complex Networks via Identifying
Skeleton in Hyperbolic Space
Predicting Long-term Dynamics of Complex Networks via Identifying
Skeleton in Hyperbolic Space
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
Arxiv
Learning complex network dynamics is fundamental for understanding, modeling,
and controlling real-world complex systems. Though great efforts have been made
to predict the future states of nodes on networks, the capability of capturing
long-term dynamics remains largely limited. This is because they overlook the
fact that long-term dynamics in complex network are predominantly governed by
their inherent low-dimensional manifolds, i.e., skeletons. Therefore, we
propose the Dynamics-Invariant Skeleton Neural Net}work (DiskNet), which
identifies skeletons of complex networks based on the renormalization group
structure in hyperbolic space to preserve both topological and dynamics
properties. Specifically, we first condense complex networks with various
dynamics into simple skeletons through physics-informed hyperbolic embeddings.
Further, we design graph neural ordinary differential equations to capture the
condensed dynamics on the skeletons. Finally, we recover the skeleton networks
and dynamics to the original ones using a degree-based super-resolution module.
Extensive experiments across three representative dynamics as well as five
real-world and two synthetic networks demonstrate the superior performances of
the proposed DiskNet, which outperforms the state-of-the-art baselines by an
average of 10.18\% in terms of long-term prediction accuracy. Code for
reproduction is available at: https://github.com/tsinghua-fib-lab/DiskNet.
Jinghua Piao、Qingmin Liao、Huandong Wang、Yong Li、Ruikun Li
Arxiv
