Hubs　are　a　set　of　highly　connected　brain　regions　which　play　an　important　role　in　the　human　brain　network.　Various　graph　measurements　and　parcellation　atlases　have　been　utilized　for　hub　identification.　However,　the　relationship　of　those　measurements　and　the　comparison　of　hub　identification　results　derived　from　different　types　of　brain　networks　are　not　clear　yet.　The　current　study　used　four　measurements　to　identify　hubs　in　75　healthy　aging　subjects＇　brain　networks.　Also,　to　figure　out　the　potential　effect　using　various　parcellation　schemes　on　the　hub　identification,　five　kinds　of　brain　networks　were　constructed,　which　were　accomplished　by　two　types　of　brain　parcellation　schemes　including　anatomical　parcellation　atlases　(AAL　atlas　and　HOA　altas)　and　random　parcellation　scheme　(uniformed　parcellation　atlas　with　32,　128　and　512　regions).　From　the　results,　we　found　that　hubs　can　be　consistently　identified　in　the　same　type　of　brain　network　regardless　of　measurements.　On　the　contrary,　hubs　were　notably　different　in　the　different　types　of　brain　networks　even　using　the　same　measurement.　Beyond　these,　hub　consistency　between　measurements　derived　from　anatomical　brain　networks　tend　to　be　relatively　stable　than　that　derived　from　uniformed　parcellated　brain　networks.　Importantly,　the　consistency　of　identification　results　of　uniformed　parcellated　brain　networks　were　also　constrained　by　the　selection　of　identification　threshold　level.　For　the　relationship　between　graph　measurements,　results　revealed　a　robust　relationship　between　vulnerability　and　other　measurements.　Our　findings　provide　a　better　understanding　to　the　effect　of　identification　measurements,　parcellation　atlases,　and　identification　thresholds　on　the　hub　identification,　which　may　offer　a　future　prospect　of　being　able　to　create　a　unified　standard　for　the　hub　identification.