We　study　the　problems　of　pricing　an　indivisible　product　to　consumers　who　are　embedded　in　a　given　social　network.　The　goal　is　to　maximize　the　revenue　of　the　seller　by　the　so-called　iterative　pricing　that　offers　consumers　a　sequence　of　prices　over　time.　The　consumers　are　assumed　to　be　impatient　in　that　they　buy　the　product　as　soon　as　the　seller　posts　a　price　not　greater　than　their　valuations　of　the　product.　The　product＇s　value　for　a　consumer　is　determined　by　two　factors:　a　fixed　consumer-specified　intrinsic　value　and　a　variable　externality　that　is　exerted　from　the　consumer＇s　neighbors　in　a　linear　way.　We　focus　on　the　scenario　of　negative　externalities,　which　captures　many　interesting　situations,　but　is　much　less　understood　in　comparison　with　its　positive　externality　counterpart.　Assuming　complete　information　about　the　network,　consumers＇　intrinsic　values,　and　the　negative　externalities,　we　prove　that　it　is　NP-hard　to　find　an　optimal　iterative　pricing,　even　for　unweighted　tree　networks　with　uniform　intrinsic　values.　Complementary　to　the　hardness　result,　we　design　a　2-approximation　algorithm　for　general　weighted　networks　with　(possibly)　nonuniform　intrinsic　values.　We　show　that,　as　an　approximation　to　optimal　iterative　pricing,　single　pricing　works　fairly　well　for　many　interesting　cases,　such　as　forests,　ErdAs-R,nyi　networks　and　Barabasi-Albert　networks,　although　its　worst-case　performance　can　be　arbitrarily　bad　in　general　networks.