Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining
  of Explanations
Harish G. Naik Jan Polster Raj Shekhar Tamás Horváth György Turán
We formulate an XAI-based model improvement approach for Graph Neural
Networks (GNNs) for node classification, called Explanation Enhanced Graph
Learning (EEGL). The goal is to improve predictive performance of GNN using
explanations. EEGL is an iterative self-improving algorithm, which starts with
a learned "vanilla" GNN, and repeatedly uses frequent subgraph mining to find
relevant patterns in explanation subgraphs. These patterns are then filtered
further to obtain application-dependent features corresponding to the presence
of certain subgraphs in the node neighborhoods. Giving an application-dependent
algorithm for such a subgraph-based extension of the Weisfeiler-Leman (1-WL)
algorithm has previously been posed as an open problem. We present experimental
evidence, with synthetic and real-world data, which show that EEGL outperforms
related approaches in predictive performance and that it has a
node-distinguishing power beyond that of vanilla GNNs. We also analyze EEGL's
training dynamics.