  The task of node classification is to infer unknown node labels, given the
labels for some of the nodes along with the network structure and other node
attributes. Typically, approaches for this task assume homophily, whereby
neighboring nodes have similar attributes and a node's label can be predicted
from the labels of its neighbors or other proximate (i.e., nearby) nodes in the
network. However, such an assumption may not always hold -- in fact, there are
cases where labels are better predicted from the individual attributes of each
node rather than the labels of its proximate nodes. Ideally, node
classification methods should flexibly adapt to a range of settings wherein
unknown labels are predicted either from labels of proximate nodes, or
individual node attributes, or partly both. In this paper, we propose a
principled approach, JANE, based on a generative probabilistic model that
jointly weighs the role of attributes and node proximity via embeddings in
predicting labels. Our experiments on a variety of network datasets demonstrate
that JANE exhibits the desired combination of versatility and competitive
performance compared to standard baselines.
