This department discusses multimedia hashing and networking. The authors summarize shallow-learning-based hashing and deep-learning-based hashing. By exploiting successful shallow-learning algorithms, state-of-the-art hashing techniques have been widely used in high-efficiency multimedia storage, indexing, and retrieval, especially in multimedia search applications on smartphone devices. The authors also introduce Multimedia Information Networks (MINets) and present one paradigm of leveraging MINets to incorporate both visual and textual information to reach a sensible event coreference resolution. The goal is to make deep learning practical in realistic multimedia applications.