  Relational learning deals with data that are characterized by relational
structures. An important task is collective classification, which is to jointly
classify networked objects. While it holds a great promise to produce a better
accuracy than non-collective classifiers, collective classification is
computational challenging and has not leveraged on the recent breakthroughs of
deep learning. We present Column Network (CLN), a novel deep learning model for
collective classification in multi-relational domains. CLN has many desirable
theoretical properties: (i) it encodes multi-relations between any two
instances; (ii) it is deep and compact, allowing complex functions to be
approximated at the network level with a small set of free parameters; (iii)
local and relational features are learned simultaneously; (iv) long-range,
higher-order dependencies between instances are supported naturally; and (v)
crucially, learning and inference are efficient, linear in the size of the
network and the number of relations. We evaluate CLN on multiple real-world
applications: (a) delay prediction in software projects, (b) PubMed Diabetes
publication classification and (c) film genre classification. In all
applications, CLN demonstrates a higher accuracy than state-of-the-art rivals.
