Meta-Graph: Few shot Link Prediction via Meta Learning

Fast adaptation to new data is one key facet of human intelligence and is an unexplored problem on graph-structured data. Few-Shot Link Prediction is a challenging task representative of real world data with evolving sub-graphs or entirely new graphs with shared structure. In this work, we present a meta-learning approach to Few Shot Link-Prediction. We further introduce Meta-Graph, a meta-learning algorithm which in addition to the global parameters learns a Graph Signature function that exploits structural information of a graph compared to other graphs from the same distribution for even faster adaptation and better convergence than vanilla Meta-Learning.