The　influence　maximization　problem　aims　to　find　some　seeds　which　can　cause　the　maximum　influence　spread　results　in　a　social　network.　Most　researches　focus　on　the　non-adaptive　strategies,　in　which　all　seeds　are　selected　at　once.　For　the　non-adaptive　strategies,　the　seeds　may　influence　other　seeds　in　the　influence　spread　process　and　make　the　waste　of　budget.　This　paper　considers　the　adaptive　strategies　and　studies　the　adaptive　influence　maximization　and　adaptive　stochastic　influence　maximization　in　the　general　feedback　model.　These　problems　select　seeds　adaptively,　and　it　completes　each　selection　after　the　fixed　observation　time-step.　In　this　paper,　we　utilize　the　adaptive　greedy　to　solve　these　problems　and　propose　a　theoretical　analysis　by　introducing　a　comparative　factor.　In　addition,　we　present　the　feasible　approximation　algorithm　using　the　reverse　sampling　technique.　Finally,　we　carry　out　experiments　on　three　networks　to　show　the　efficiency　of　adaptive　strategies.　©　2022　Elsevier　B.V.