In　recent　years,　a　number　of　out-of-core　graph　processing　systems　have　been　proposed　to　process　graphs　with　billions　of　edges　on　just　one　commodity　computer,　due　to　their　high　cost　efficiency.　To　obtain　the　better　performance,　these　systems　adopt　a　full　I/O　model　that　accesses　all　edges　during　the　computation　to　avoid　the　ineffectiveness　of　random　I/Os.　Although　this　model　ensures　good　I/O　access　locality,　it　loads　a　large　number　of　useless　edges　when　running　graph　algorithms　that　only　require　a　small　portion　of　edges　in　each　iteration.　A　natural　method　to　solve　this　problem　is　the　on-demand　I/O　model　that　only　accesses　the　active　edges.　However,　this　method　only　works　well　for　the　graph　algorithms　with　very　few　active　edges,　since　the　I/O　cost　will　grow　rapidly　as　the　number　of　active　edges　increases　due　to　larger　amount　of　random　I/Os.　In　this　paper,　we　present　HUS-Graph,　an　efficient　out-of-core　graph　processing　system　to　address　the　above　I/O　issues　and　achieve　a　good　balance　between　I/O　amount　and　I/O　access　locality.　HUS-Graph　first　adopts　a　hybrid　update　strategy　including　two　update　models,　Row-oriented　Push　(ROP)　and　Column-oriented　Pull　(COP).　It　can　adaptively　select　the　optimal　update　model　for　the　graph　algorithms　that　have　different　computation　and　I/O　features,　based　on　an　I/O-based　performance　prediction　method.　Furthermore,　HUS-Graph　proposes　a　dual-block　representation　to　organize　graph　data,　which　ensures　good　access　locality.　Extensive　experimental　results　show　that　HUS-Graph　outperforms　existing　out-of-core　systems　by　1.4x-23.1x.　©　2018　Association　for　Computing　Machinery.