Discrete　network　embedding　emerged　recently　as　a　new　direction　of　network　representation　learning.　Compared　with　traditional　network　embedding　models,　discrete　network　embedding　aims　to　compress　model　size　and　accelerate　model　inference　by　learning　a　set　of　short　binary　codes　for　network　vertices.　However,　existing　discrete　network　embedding　methods　usually　assume　that　the　network　structures　(e.g.,　edge　weights)　are　readily　available.　In　real-world　scenarios　such　as　social　networks,　sometimes　it　is　impossible　to　collect　explicit　network　structure　information　and　it　usually　needs　to　be　inferred　from　implicit　data　such　as　information　cascades　in　the　networks.　To　address　this　issue,　we　present　an　end-to-end　discrete　network　embedding　model　for　latent　networks　(DELN)　that　can　learn　binary　representations　from　underlying　information　cascades.　The　essential　idea　is　to　infer　a　latent　Weisfeiler-Lehman　proximity　matrix　that　captures　node　dependence　based　on　information　cascades　and　then　to　factorize　the　latent　Weisfiler-Lehman　matrix　under　the　binary　node　representation　constraint.　Since　the　learning　problem　is　a　mixed　integer　optimization　problem,　an　efficient　maximal　likelihood　estimation　based　cyclic　coordinate　descent　(MLE-CCD)　algorithm　is　used　as　the　solution.　Experiments　on　real-world　datasets　show　that　the　proposed　model　outperforms　the　state-of-the-art　network　embedding　methods.