In　multi-media　and　social　media　communities,　web　topic　detection　poses　two　main　difficulties　that　conventional　approaches　can　barely　handle:　1)　there　are　large　inter-topic　variations　among　web　topics;　2)　supervised　information　is　rare　to　identify　the　real　topics.　In　this　paper,　we　address　these　problems　from　the　similarity　diffusion　perspective　among　objects　on　web,　and　present　a　clustering-like　pattern　across　similarity　cascades　(SCs).　SCs　are　a　series　of　subgraphs　generated　by　truncating　a　weighted　graph　with　a　set　of　thresholds,　and　then　maximal　cliques　are　used　to　describe　the　topic　candidates.　Poisson　deconvolution　is　adopted　to　efficiently　identify　the　real　topics　from　these　topic　candidates.　Experiments　demonstrate　that　our　approach　outperforms　the　state-of-the-arts　on　two　datasets.　In　addition,　we　report　accuracy　v.s.　false　positives　per　topic　(FPPT)　curves　for　performance　evaluation.　To　our　knowledge,　this　is　the　first　complete　evaluation　of　web　topic　detection　at　the　topic-wise　level,　and　it　establishes　a　new　benchmark　for　this　problem.