Big　data　applications　increasingly　rely　on　the　analysis　of　large　graphs.　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,　by　efficiently　using　the　secondary　storage　(e.g.,　hard　disk,　SSD).　On　the　other　hand,　the　vertex-centric　computing　model　is　extensively　used　in　graph　processing　thanks　to　its　good　applicability　and　expressiveness.　Unfortunately,　when　implementing　vertex-centric　model　for　out-of-core　graph　processing,　the　large　number　of　random　memory　accesses　required　to　construct　subgraphs　lead　to　a　serious　performance　bottleneck　that　substantially　weakens　cache　access　locality　and　thus　leads　to　very　long　waiting　time　experienced　by　users　for　the　computing　results.　In　this　paper,　we　propose　an　efficient　out-of-core　graph　processing　system,　LOSC,　to　substantially　reduce　the　overhead　of　subgraph　construction　without　sacrificing　the　underlying　vertex-centric　computing　model.　LOSC　proposes　a　locality-optimized　subgraph　construction　scheme　that　significantly　improves　the　in-memory　data　access　locality　of　the　subgraph　construction　phase.　Furthermore,　LOSC　adopts　a　compact　edge　storage　format　and　a　lightweight　replication　of　vertices　to　reduce　I/O　traffic　and　improve　computation　efficiency.　Extensive　evaluation　results　show　that　LOSC　is　respectively　6.9x　and　3.5x　faster　than　GraphChi　and　GridGraph,　two　state-of-the-art　out-of-core　systems.　©　2019　Association　for　Computing　Machinery.