Network-informed pathways to sustainable energy Access: A graph mining approach for accelerating clean energy transitions in developing countries

This study applies network analysis and graph mining techniques to examine sustainable energy access dynamics in Ghana, revealing structural patterns that can accelerate renewable energy transitions in developing countries. Using household survey data from 4800 households across 16 regions, the study constructs similarity networks based on multidimensional sustainable energy characteristics and applies community detection algorithms to identify distinct household clusters for targeted renewable energy deployment strategies. The household similarity network exhibits small-world properties with high clustering (0.743) and short average path lengths (2.12), indicating efficient pathways for clean energy information transmission within tightly connected local communities. Community detection identifies seven distinct household archetypes ranging from urban clean energy-advantaged (14.8% of sample, mean Energy Poverty Index (EPI) 0.258, 94.1% electricity access, 71.9% clean cooking access) to rural sustainable energy-poor (26.8% of sample, mean EPI 0.778, 23.5% electricity access, 8.2% clean cooking access) with high modularity (0.6925). Centrality analysis reveals that high-centrality households exhibit intermediate sustainable energy characteristics, positioning them as critical bridges for clean energy diffusion, making them optimal targets for renewable energy demonstration projects. Bootstrap validation demonstrates high stability, confirming robust community structures. These findings demonstrate that sustainable energy access exhibits systematic structural properties requiring network-informed renewable energy policies. Community-based interventions targeting high-centrality households could generate spillover effects throughout networks, offering efficient pathways for renewable energy adoption while supporting climate mitigation objectives. The framework provides a replicable methodology for sustainable energy research, contributing to SDG 7 achievement and accelerating clean energy transitions.