Informative　proteins　are　the　proteins　that　play　critical　functional　roles　inside　cells.　They　are　the　fundamental　knowledge　of　translating　bioinformatics　into　clinical　practices.　Many　methods　of　identifying　informative　biomarkers　have　been　developed　which　are　heuristic　and　arbitrary,　without　considering　the　dynamics　characteristics　of　biological　processes.　In　this　paper,　we　present　a　generative　model　of　identifying　the　informative　proteins　by　systematically　analyzing　the　topological　variety　of　dynamic　protein-protein　interaction　networks　(PPINs).　In　this　model,　the　common　representation　of　multiple　PPINs　is　learned　using　a　deep　feature　generation　model,　based　on　which　the　original　PPINs　are　rebuilt　and　the　reconstruction　errors　are　analyzed　to　locate　the　informative　proteins.　Experiments　were　implemented　on　data　of　yeast　cell　cycles　and　different　prostate　cancer　stages.　We　analyze　the　effectiveness　of　reconstruction　by　comparing　different　methods,　and　the　ranking　results　of　informative　proteins　were　also　compared　with　the　results　from　the　baseline　methods.　Our　method　is　able　to　reveal　the　critical　members　in　the　dynamic　progresses　which　can　be　further　studied　to　testify　the　possibilities　for　biomarker　research.