Protein-protein interactions (PPIs) form highly dynamic and context-specific networks that vary across cell types and perturbation states. Mapping and understanding these context-dependent interactomes are essential for unraveling cellular organization and function. This review examines computational and experimental approaches for mapping and analyzing context-specific PPI networks. Computational methods refine global interactomes using context-specific expression data, literature curation, or machine learning-based integration of omics datasets, thereby constructing interactomes that consist of both physical and functional associations. Experimental approaches, including crosslinking mass spectrometry, cofractionation mass spectrometry, and denaturation-based mass spectrometry methods, directly capture physical PPIs within defined biological contexts. This review further discusses analytical frameworks for extracting biological insights from these networks, including protein complex detection, network embedding, differential analysis, functional module detection, and biomarker discovery. Finally, future directions are highlighted involving multiomic integration, enhanced spatial and temporal resolution, and artificial intelligence-driven network interpretation for further enriching the biological insights that can be obtained from context-specific PPI networks.