Inspired by the recently introduced graph traversal networks (Moon et al., 2019) which output discrete graph operations given input contexts, we formulate our MGN as follows.
given a set of initial memory slots (m) and a query (q), the MgN model outputs a sequence path of walk steps (p) within Mg to attend to relevant nodes or expand initial memory slots ( Figure 3): we define the attention-based graph decoder model which prunes unattended paths, which effectively reduce the search space for memory expansion.