(Ferret, 2017) apply distributional thesaurus embedding for synonym extraction and expansion tasks whereas Jana and Goyal (2018a) use it to improve the state-of-theart performance of word similarity/relatedness tasks, word analogy task etc. Thus, a natural question arises as to whether network embeddings should be more effective than the handcrafted network features used by <cite>Jana and Goyal (2018b)</cite> for cohyponymy detection. Being motivated by this connection, we investigate how the information captured by network representation learning methodologies on distributional thesaurus can be used in discriminating word pairs having co-hyponymy relation from the word pairs having hypernymy, meronymy relation or any random pair of words. Evaluation results: We evaluate the usefulness of DT embeddings against three benchmark datasets for cohyponymy detection (Weeds et al., 2014; Santus et al., 2016; <cite>Jana and Goyal, 2018b)</cite> , following their experimental setup.