  Graph neural networks are promising architecture for learning and inference
with graph-structured data. Yet difficulties in modelling the ``parts'' and
their ``interactions'' still persist in terms of graph classification, where
graph-level representations are usually obtained by squeezing the whole graph
into a single vector through graph pooling. From complex systems point of view,
mixing all the parts of a system together can affect both model
interpretability and predictive performance, because properties of a complex
system arise largely from the interaction among its components. We analyze the
intrinsic difficulty in graph classification under the unified concept of
``resolution dilemmas'' with learning theoretic recovery guarantees, and
propose ``SLIM'', an inductive neural network model for Structural Landmarking
and Interaction Modelling. It turns out, that by solving the resolution
dilemmas, and leveraging explicit interacting relation between component parts
of a graph to explain its complexity, SLIM is more interpretable, accurate, and
offers new insight in graph representation learning.
