A　dynamically　weighted　directed　network　(DWDN)　is　frequently　encountered　in　various　big　data-related　applications　like　a　terminal　interaction　pattern　analysis　system　(TIPAS)　concerned　in　this　study.　It　consists　of　large-scale　dynamic　interactions　among　numerous　nodes.　As　the　involved　nodes　increase　drastically,　it　becomes　impossible　to　observe　their　full　interactions　at　each　time　slot,　making　a　resultant　DWDN　High　Dimensional　and　Incomplete　(HDI).　An　HDI　DWDN,　in　spite　of　its　incompleteness,　contains　rich　knowledge　regarding　involved　nodes＇　various　behavior　patterns.　To　extract　such　knowledge　from　an　HDI　DWDN,　this　paper　proposes　a　novel　Alternating　direction　method　of　multipliers　(ADMM)-based　Nonnegative　Latent-factorization　of　Tensors　(ANLT)　model.　It　adopts　three-fold　ideas:　a)　building　a　data　density-oriented　augmented　Lagrangian　function　for　efficiently　handling　an　HDI　tensor＇s　incompleteness　and　nonnegativity;　b)　splitting　the　optimization　task　in　each　iteration　into　an　elaborately　designed　subtask　series　where　each　one　is　solved　based　on　the　previously　solved　ones　following　the　ADMM　principle　to　achieve　fast　convergence;　and　c)　theoretically　proving　that　its　convergence　is　guaranteed　with　its　efficient　learning　scheme.　Experimental　results　on　six　DWDNs　from　real　applications　demonstrate　that　the　proposed　ANLT　outperforms　state-of-the-art　models　significantly　in　both　computational　efficiency　and　prediction　accuracy　for　missing　links　of　an　HDI　DWDN.　Hence,　this　study　proposes　a　novel　and　efficient　approach　to　large-scale　DWDN　representation.