Relational dynamic memory networks
  Neural networks excel in detecting regular patterns but are less successful
in representing and manipulating complex data structures, possibly due to the
lack of an external memory. This has led to the recent development of a new
line of architectures known as Memory-Augmented Neural Networks (MANNs), each
of which consists of a neural network that interacts with an external memory
matrix. However, this RAM-like memory matrix is unstructured and thus does not
naturally encode structured objects. Here we design a new MANN dubbed
Relational Dynamic Memory Network (RMDN) to bridge the gap. Like existing
MANNs, RMDN has a neural controller but its memory is structured as
multi-relational graphs. RMDN uses the memory to represent and manipulate
graph-structured data in response to query; and as a neural network, RMDN is
trainable from labeled data. Thus RMDN learns to answer queries about a set of
graph-structured objects without explicit programming. We evaluate the
capability of RMDN on several important prediction problems, including software
vulnerability, molecular bioactivity and chemical-chemical interaction. Results
demonstrate the efficacy of the proposed model.
