Data and supplementary materials for: Thermodynamic stability and structural transitions in virus–host networks

Virus–Host Interaction Networks: Data and Structural Analysis This repository contains datasets and supplementary materials for the study of virus–host interaction networks in: Homo sapiens Mus musculus Gallus gallus The data support analyses of network structure, robustness under node removal, and thermodynamic-inspired descriptors. 📦 Repository Structure 🔗 Network Data Weighted and directed graphs representing virus–host interactions are provided in multiple formats for compatibility with different tools: .graphml (recommended) .gexf .gml .json .csv Files: graph_homo.* graph_mus.* graph_gallus.* (to be added) Each graph encodes: Nodes: virus-associated or host-associated entities Edges: interactions with associated weights (e.g., confidence scores) 📊 Simulation Results Results of network degradation under node removal: Targeted removal (highest-degree nodes) *-hub-simulation_results.csv Random removal (averaged over multiple runs) *-rnd-simulation_results.csv These files contain step-by-step evolution of network properties. 🔥 Heat Capacity Data Spectral-based thermodynamic analysis: *-hub-heat_capacity.csv These datasets capture transition-like behavior derived from adjacency spectra. 🎥 Network Dynamics Animations illustrating structural evolution during node removal: network_animation_homo_hub.mp4 — targeted removal network_animation_gallus_rnd.mp4 — random removal ⚙️ Methods Overview Networks are weighted and directed Node removal strategies: Targeted: sequential removal of highest-degree nodes Random: removal of randomly selected nodes (averaged over multiple realizations) Computed quantities include: Network density Degree and strength distributions Assortativity Strongly connected components Susceptibility (node-level heterogeneity measure) Effective temperature (based on edge-weight dispersion) Energy-related metrics Note: Thermodynamic quantities (magnetization, susceptibility, temperature) are used as structural descriptors and do not represent equilibrium physical systems. 🧪 Reproducibility The dataset enables: Reproduction of reported results Independent validation of network properties Application of alternative network analysis techniques Recommended tools: Python (networkx, numpy, scipy, matplotlib) Gephi / Cytoscape for visualization 📁 File Formats Format Description GraphML Rich, structured graph format GEXF Gephi-compatible format GML Lightweight graph format JSON Programmatic use CSV Tabular data (edges / metrics) MP4 Network evolution animations 📖 Citation If you use this dataset, please cite: [Citation to be added upon publication or preprint] 📬 Contact For questions or collaboration inquiries, please contact the author(s). 📜 License Specify the license under which the data are shared (e.g., CC BY 4.0).