  In this paper, we tackle the real-world problem of predicting Yelp
star-review rating based on business features (such as images, descriptions),
user features (average previous ratings), and, of particular interest, network
properties (which businesses has a user rated before). We compare multiple
models on different sets of features -- from simple linear regression on
network features only to deep learning models on network and item features.
  In recent years, breakthroughs in deep learning have led to increased
accuracy in common supervised learning tasks, such as image classification,
captioning, and language understanding. However, the idea of combining deep
learning with network feature and structure appears to be novel. While the
problem of predicting future interactions in a network has been studied at
length, these approaches have often ignored either node-specific data or global
structure.
  We demonstrate that taking a mixed approach combining both node-level
features and network information can effectively be used to predict Yelp-review
star ratings. We evaluate on the Yelp dataset by splitting our data along the
time dimension (as would naturally occur in the real-world) and comparing our
model against others which do no take advantage of the network structure and/or
deep learning.
