Experimental Data Utilized in Research Titled Unveiling Universal Dynamical Patterns in Complex Networks via an Intelligent Search-Based Algorithm

Dataset Overview

This dataset documents all network dynamical models and network structures employed in the research titled Unveiling Universal Dynamical Patterns in Complex Networks via an Intelligent Search-Based Algorithm. It is organized into two main components, network topologies and network dynamics.

1. Network Topologies

The dataset features three fundamental network structures generated using NetworkX, Erdős-Rényi (ER) network, Watts-Strogatz (WS) networks, and Barabási-Albert (BA) networks. Each network is represented by its adjacency matrix, formatted as .csv files. These topologies collectively capture essential network properties including small-worldness, clustering, and scale-free characteristics.

2. Network Dynamics

This section provides dynamical data from five canonical network models including the heterogeneous Kuramoto oscillator, the heterogeneous Rössler oscillator, the susceptible-infected-susceptible (SIS) epidemic model, the Michaelis-Menten kinetics, and the FitzHugh-Nagumo (FHN) model. The dataset includes both nodal state data and numerical time derivatives for each model. State data was generated using Scipy, while numerical derivatives were computed using the five-point approach. All data files are formatted as .csv. Additionally, noise data, topologies with missing and spurious links are provided under the FHN directory, enabling robustness testing.