Predicting Higher-Order Dynamics With Unknown Hypergraph Topology

doi:10.1109/TCSI.2024.3513406

Predicting Higher-Order Dynamics With Unknown Hypergraph Topology

2024-01-01

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DOI：10.1109/TCSI.2024.3513406

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Predicting future dynamics on networks is challenging, especially when the complete and accurate network topology is difficult to obtain in real-world scenarios. Moreover, the higher-order interactions among nodes, which have been found in a wide range of systems in recent years, such as the nets connecting multiple modules in circuits, further complicate accurate prediction of dynamics on hypergraphs. In this work, we proposed a two-step method called the topology-agnostic higher-order dynamics prediction (TaHiP) algorithm. The observations of nodal states of the target hypergraph are used to train a surrogate matrix, which is then employed in the dynamical equation to predict future nodal states in the same hypergraph, given the initial nodal states. TaHiP outperforms three latest Transformer-based prediction models in different real-world hypergraphs. Furthermore, experiments in synthetic and real-world hypergraphs show that the prediction error of the TaHiP algorithm increases with mean hyperedge size of the hypergraph, and could be reduced if the hyperedge size distribution of the hypergraph is known.

Keyword:

Power system dynamics

Topology

Heuristic algorithms

Mathematical models

Predictive models

Accuracy

Prediction algorithms

Vectors

Integrated circuit modeling

Training

Nonlinear system

dynamics on networks

predicting higher-order dynamics

contagion

hypergraph

Predicting Higher-Order Dynamics With Unknown Hypergraph Topology

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