title: Link Weight Prediction with Node Embeddings
abstract: Application of deep learning has been successful in various domains such as im-
  age recognition, speech recognition and natural language processing. However,
  the research on its application in graph mining is still in an early stage. Here we
  present the first generic deep learning approach to the graph link weight prediction
  problem based on node embeddings. We evaluate this approach with three differ-
  ent node embedding techniques experimentally and compare its performance with
  two state-of-the-art non deep learning baseline approaches. Our experiment re-
  sults suggest that this deep learning approach outperforms the baselines by up to
  70% depending on the dataset and embedding technique applied. This approach
  shows that deep learning can be successfully applied to link weight prediction to
  improve prediction accuracy.
authors: Yuchen Hou Lawrence B. Holder
hIndex: Lawrence B. Holder 15