Neural networks are increasingly used for graph classification in a variety
of contexts. Social media is a critical application area in this space, however
the characteristics of social media graphs differ from those seen in most
popular benchmark datasets. Social networks tend to be large and sparse, while
benchmarks are small and dense. Classically, large and sparse networks are
analyzed by studying the distribution of local properties. Inspired by this, we
introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent
local features, bins nodes together along 1-D cross sections of the feature
space, and classifies the graph based on this multi-channel histogram. We show
that Graph-Hist improves state of the art performance on true social media
benchmark datasets, while still performing well on other benchmarks. Finally,
we demonstrate Graph-Hist's performance by conducting bot detection in social
media. While sophisticated bot and cyborg accounts increasingly evade
traditional detection methods, they leave artificial artifacts in their
conversational graph that are detected through graph classification. We apply
Graph-Hist to classify these conversational graphs. In the process, we confirm
that social media graphs are different than most baselines and that Graph-Hist
outperforms existing bot-detection models