Despite　the　massive　growth　of　social　media　on　the　Internet,　the　process　of　organizing,　understanding,　and　monitoring　user　generated　content　(UGC)　has　become　one　of　the　most　pressing　problems　in　today＇s　society.　Discovering　topics　on　the　web　from　a　huge　volume　of　UGC　is　one　of　the　promising　approaches　to　achieve　this　goal.　Compared　with　classical　topic　detection　and　tracking　in　news　articles,　identifying　topics　on　the　web　is　by　no　means　easy　due　to　the　noisy,　sparse,　and　less-constrained　data　on　the　Internet.　In　this　paper,　we　investigate　methods　from　the　perspective　of　similarity　diffusion,　and　propose　a　clustering-like　pattern　across　similarity　cascades　(SCs).　SCs　are　a　series　of　subgraphs　generated　by　truncating　a　similarity　graph　with　a　set　of　thresholds,　and　then　maximal　cliques　are　used　to　capture　topics.　Finally,　a　topic-restricted　similarity　diffusion　process　is　proposed　to　efficiently　identify　real　topics　from　a　large　number　of　candidates.　Experiments　demonstrate　that　our　approach　outperforms　the　state-of-the-art　methods　on　three　public　data　sets.