  Detecting communities has long been popular in the research on networks. It
is usually modeled as an unsupervised clustering problem on graphs, based on
heuristic assumptions about community characteristics, such as edge density and
node homogeneity. In this work, we doubt the universality of these widely
adopted assumptions and compare human labeled communities with machine
predicted ones obtained via various mainstream algorithms. Based on supportive
results, we argue that communities are defined by various social patterns and
unsupervised learning based on heuristics is incapable of capturing all of
them. Therefore, we propose to inject supervision into community detection
through Community Oriented Network Embedding (CONE), which leverages limited
ground-truth communities as examples to learn an embedding model aware of the
social patterns underlying them. Specifically, a deep architecture is developed
by combining recurrent neural networks with random-walks on graphs towards
capturing social patterns directed by ground-truth communities. Generic
clustering algorithms on the embeddings of other nodes produced by the learned
model then effectively reveals more communities that share similar social
patterns with the ground-truth ones.
