We address the problem of detecting characteristic patterns in communication networks. We introduce a scalable approach based on set-system discrepancy. By implicitly labeling each network edge with the sequence of times in which its two endpoints communicate, we view an entire communication network as a set-system. This view allows us to use combinatorial discrepancy as a mechanism to "observe" system behavior at different time scales. We illustrate our approach, called Discrepancy-based Novelty Detector (DND), on networks obtained from emails, blue tooth connections, IP traffic, and tweets. DND has almost linear runtime complexity and linear storage complexity in the number of communications. Examples of novel discrepancies that it detects are (i) asynchronous communications and (ii) disagreements in the firing rates of nodes and edges relative to the communication network as a whole.