  Graph Retrieval has witnessed continued interest and progress in the past few
years. In thisreport, we focus on neural network based approaches for Graph
matching and retrieving similargraphs from a corpus of graphs. We explore
methods which can soft predict the similaritybetween two graphs. Later, we
gauge the power of a particular baseline (Shortest Path Kernel)and try to model
it in our product graph random walks setting while making it more generalised.
