  This work studies the entity-wise topical behavior from massive network logs.
Both the temporal and the spatial relationships of the behavior are explored
with the learning architectures combing the recurrent neural network (RNN) and
the convolutional neural network (CNN). To make the behavioral data appropriate
for the spatial learning in CNN, several reduction steps are taken to form the
topical metrics and place them homogeneously like pixels in the images. The
experimental result shows both the temporal- and the spatial- gains when
compared to a multilayer perceptron (MLP) network. A new learning framework
called spatially connected convolutional networks (SCCN) is introduced to more
efficiently predict the behavior.
