The betweenness (bc; grsg_lcp_betweenness_centrality) defines the importance of a node in a graph based on how many times it occurs in the shortest path between all pairs of nodes. In other words, a node is important if it is included in many shortest paths between other nodes because it serves as a bridge between different parts of the graph. The data were defined from least-cost paths (LCPs) constructed into minimum spanning trees (MSTs). The bc identified major corridors spanning the sage-grouse range where nodes had a larger number of connections with other nodes, reflecting regions where leks potentially play larger roles of sage-grouse continuity based on graph theory analytics. We identified a threshold of the bc normalized value (>0.028) where patterns of network connectivity occurred in our graph. Leks identified with a bc value greater than our threshold were buffered by 15 km (inter-patch movement distance and distance of genetic flow), resulting in this dataset. Betweenness centrality captured corridors of leks with a higher density of sage-grouse, which we used to evaluate our derived population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths (LCP) between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.