Matching Models for Graph Retrieval
This work addresses graph retrieval for researchers and practitioners, but it appears incremental as it builds on existing baselines without claiming major breakthroughs.
The paper tackles the problem of graph retrieval by exploring neural network approaches for matching and retrieving similar graphs, focusing on soft similarity prediction and generalizing a baseline method (Shortest Path Kernel) in a product graph random walks setting.
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.