  Interpretability has emerged as a crucial aspect of building trust in machine
learning systems, aimed at providing insights into the working of complex
neural networks that are otherwise opaque to a user. There are a plethora of
existing solutions addressing various aspects of interpretability ranging from
identifying prototypical samples in a dataset to explaining image predictions
or explaining mis-classifications. While all of these diverse techniques
address seemingly different aspects of interpretability, we hypothesize that a
large family of interepretability tasks are variants of the same central
problem which is identifying \emph{relative} change in a model's prediction.
This paper introduces MARGIN, a simple yet general approach to address a large
set of interpretability tasks MARGIN exploits ideas rooted in graph signal
analysis to determine influential nodes in a graph, which are defined as those
nodes that maximally describe a function defined on the graph. By carefully
defining task-specific graphs and functions, we demonstrate that MARGIN
outperforms existing approaches in a number of disparate interpretability
challenges.
