  In this paper, we study the topical behavior in a large scale. We use the
network logs where each entry contains the entity ID, the timestamp, and the
meta data about the activity. Both the temporal and the spatial relationships
of the behavior are explored with the deep 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 the CNN, we
propose several reduction steps to form the topical metrics and to place them
homogeneously like pixels in the images. The experimental result shows both
temporal and spatial gains when compared against a multilayer perceptron (MLP)
network. A new learning framework called the spatially connected convolutional
networks (SCCN) is introduced to predict the topical metrics more efficiently.
