Recently,　multi-view　clustering　methods　have　garnered　considerable　attention　and　have　been　applied　in　various　domains.　However,　in　practical　scenarios,　some　samples　may　lack　specific　views,　giving　rise　to　the　challenge　of　incomplete　multi-view　clustering.　While　some　methods　focus　on　completing　missing　data,　incorrect　completion　can　negatively　affect　representation　learning.　Moreover,　separating　completion　and　representation　learning　prevents　the　attainment　of　an　optimal　representation.　Other　methods　eschew　completion　but　singularly　concentrate　on　either　feature　information　or　graph　information,　thus　failing　to　achieve　comprehensive　representations.　To　address　these　challenges,　we　propose　a　graph-guided,　imputation-free　method　for　incomplete　multi-view　clustering.　Unlike　completion-based　methods,　our　approach　aims　to　maximize　the　utilization　of　existing　information　by　simultaneously　considering　feature　and　graph　information.　This　is　realized　through　the　feature　learning　component　and　the　graph　learning　component.　Introducing　a　degradation　network,　the　former　reconstructs　view-specific　representations　proximate　to　available　samples　from　a　unified　representation,　seamlessly　integrating　feature　information　into　the　unified　representation.　Leveraging　the　semi-supervised　idea,　the　latter　utilizes　reliable　graph　information　from　available　samples　to　guide　the　learning　of　the　unified　representation.　These　two　components　collaborate　to　acquire　a　comprehensive　unified　representation　for　multi-view　clustering.　Extensive　experiments　conducted　on　real　datasets　demonstrate　the　effectiveness　and　competitiveness　of　the　proposed　method　when　compared　with　other　state-of-the-art　methods.　Our　code　will　be　released　on　https://github.com/yff-java/GIMVC/.