The　Graph　Partitioning　Problem　(GPP)　is　a　classical　combinatorial　optimization　problem　that　has　been　extensively　researched.　In　recent　years,　many　methods　for　solving　the　GPP　have　been　proposed,　which　can　be　divided　into　direct　partitioning　approaches　and　iterative　improvement　approaches.　Direct　partitioning　approaches　directly　give　a　complete　partition　of　the　input　graph.　Iterative　improvement　approaches　require　further　adjustment　of　feasible　solutions　based　on　the　result　of　direct　partitioning　approaches　in　order　to　obtain　a　better　performance.　In　this　paper,　we　summarize　the　recent　trends　in　algorithms　and　applications　for　GPP.　In　addition,　we　propose　a　graph　partitioning　algorithm　based　on　semi-supervised　learning　in　combination　with　graph　filtering　methods.　©　2023　The　Authors.　Published　by　Elsevier　B.V.　This　is　an　open　access　article　under　the　CC　BY-NC-ND　license　(https://creativecommons.org/licenses/by-nc-nd/4.0)　Peer-review　under　responsibility　of　the　scientific　committee　of　the　Tenth　International　Conference　on　Information　Technology　and.