Compared　with　the　Static　Graph　Neural　Networks(SGNNs),　the　Dynamic　Graph　Neural　Networks(DGNNs)　have　an　additional　temporal　dimension,　which　can　evolve　along　the　time　dimension,　so　DGNNs　have　more　powerful　and　flexible　modeling　capabilities.　DGNNs　are　widely　used　in　the　classical　graph　domain,　such　as　social　network　recommendations,　chemical　molecular　structure　predictions　and　so　on.　They　are　also　succeeding　in　areas　such　as　social　event　prediction,　video　classification,　traffic　prediction.　Although　the　advantages　of　DGNNs　have　been　verified　in　various　aspects,　there　are　still　relatively　few　relevant　review　studies.　This　paper　describes　the　reason　why　DGNNs　can　process　temporal　data,　which　lays　a　foundation　for　subsequent　understanding　of　various　DGNNs　models.　Based　on　temporal　models　and　GNN　models,　the　whole　design　idea　of　DGNNs　is　provided　for　readers　through　detailed　description　of　encoding　and　decoding　process　and　link　predictions　of　various　DGNNs.　Finally,　we　are　dedicated　to　mining　the　commonness　of　DGNN　framework　from　different　task　scenarios,　and　presenting　a　detailed　DGNNs　overview　for　readers.　©　2022　IEEE.