Various　Web-based　social　network　data　reflect　user　interests　from　multiple　perspectives　in　a　distributed　environment.　They　need　to　be　integrated　for　better　user　modelling　and　personalized　services.　We　argue　that　in　different　scenarios,　different　social　networks　play　different　roles　and　their　degrees　of　importance　are　not　equivalent.　Hence,　ranking　strategies　among　different　social　network　data　sources　are　needed.　In　addition,　combining　different　social　network　data　can　produce　interesting　subsets　of　these　data　with　different　levels　of　importance.　In　this　paper,　we　propose　social　network　data　ranking　and　composition　strategies,　we　validate　the　proposed　methods　by　collaboration　network　data　(Semantic　Web　Dog　Food)　and　micro-blogging　data　(from　Twitter),　then　we　use　the　ranked　and　composed　results　for　developing　a　Web-based　personalized　academic　visit　recommendation　system　to　show　their　potential　effectiveness.　©　2012　ACM.