Reverse　percolation　analyzes　the　overall　connectivity　of　a　network　after　the　addition　of　nodes　or　edges　at　predefined　probabilities,　which　parallels　the　significance　and　application　potential　of　traditional　percolation　theory.　This　paper　explores　the　intersection　between　reverse　percolation　and　network　growth.　It　discusses　the　addition　of　a　set　of　new　nodes　and　edges　simultaneously,　offering　insight　into　two　distinct　scenarios:　random　attachment,　where　all　potential　new　edges　have　equal　occupation　probability,　and　preferential　attachment,　where　the　occupation　probability　of　a　potential　new　edge　is　proportional　to　the　degree　of　the　original　network　node　it　connects　to.　Reverse　percolation　analytic　models　are　developed　for　these　scenarios　to　compute　the　spanning　cluster　fraction　and　the　percolation　threshold.　The　accuracy　of　these　models　are　evaluated　across　multiple　real-world　networks.　Furthermore,　the　possibility　of　generating　scale-free　networks　through　single-step　node　and　edge　additions　based　on　the　preferential　attachment　mechanism　is　investigated.　The　results　reveal　that　the　double-unknown　generating-function-based　model　proposed　in　this　paper　ensures　universal　applicability　and　accurate　prediction　across　both　investigated　scenarios.　In　the　context　of　preferential　attachment,　the　simultaneous　addition　of　nodes　and　edges　may　always　produce　a　proportion　of　networks　exhibiting　scare-free　structures.　However,　irrespective　of　the　emergence　of　scale-free　properties,　the　connectivity　status　of　these　networks　can　be　effectively　predicted　using　the　proposed　reverse　percolation　model.　©　2024