Protein–protein interaction (PPI) networks underpin most cellular processes and are central to understanding disease mechanisms and therapeutic opportunities. However, methodological heterogeneity (data acquisition, curation, graph construction, noise filtering, and downstream analytics) often limits reproducibility and cross-study comparability. This Methods Collection assembles cutting-edge, open, and reproducible protocols for constructing and analyzing PPI networks, focusing on graph-theoretic foundations.

Contributed articles will cover the following:

1. Standardized pipelines for integrating multi-source interactomes and context-specific subnetworks
2. Robust preprocessing (confidence scoring, bias assessment, batch/assay harmonization)
3. Graph embeddings and metric-space formulations for topology-aware inference
4. Dominating-set and related covering/controllability frameworks for identifying minimal driver sets
5. Centrality and community-structure analyses linked to pathway enrichment
6. End-to-end workflows that tie computational predictions to experimental validation

By emphasizing transparent code, shareable datasets, parameter logging, and environment capture, the collection aims to raise methodological standards, facilitate fair comparisons across tools, and accelerate translation of PPI network analyses to target discovery in oncology and other disease areas.