Characterizing the Influence of Graph Elements

Understand

Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of training instances.

• It is an efficient and useful post-hoc method for studying the interpretability of machine learning models without the need for expensive model re-training.
• Recently, graph convolution networks (GCNs), which operate on graph data, have attracted a great deal of attention.
• However, there is no preceding research on the influence functions of GCNs to shed light on the effects of removing training nodes/edges from an input graph.