Discovering　communities　in　large-scale　social　networks　efficiently　and　accurately　is　one　of　the　challenges　in　social　network　data　mining.　We　propose　a　clustering　algorithm　to　discover　social　network　communities　based　on　the　propagation　of　local　and　global　information.　Three　strategies,　namely,　localizing　propagation　of　affinity　messages,　relaxing　self-exemplar　constraints,　and　hierarchical　processing,　are　employed　in　the　algorithm　to　achieve　reasonable　time　and　space　complexities　in　social　networks.　The　local　and　global　information　is　represented　by　the　k-path　edge　centrality　incorporated　in　the　similarity　calculation.　The　standalone　algorithm　is　extended　to　provide　parallel　implementations　based　on　the　MapReduce　model　to　accelerate　processing　in　large-scale　networks.　Two　well-known　parallel　computation　frameworks,　Hadoop　and　Spark,　are　adopted　to　implement　the　parallel　algorithm.　Experiments　performed　on　artificial　and　real　social　network　datasets　show　that　the　proposed　algorithms　can　achieve　near-linear　time　and　space　complexities　with　comparative　clustering　accuracy.　©　2015　Elsevier　Inc.