@conference {103,
	title = {Trimming the Hairball: Edge Cutting Strategies for Making Dense Graphs Usable},
	booktitle = {2018 IEEE International Conference on Big Data (Big Data)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The application of modern NLP and ML techniques to large-scale datasets can generate implicit graphs that are so densely connected as to be unusable when rendered as node-link diagrams. We present a two-stage approach to extracting usable, map-like layouts from large, dense input graphs. This approach uses edge-cutting strategies based on node and edge metrics to reduce a graph to a skeletal structure showing only essential relationships, before filling in the resulting communities to create dense but usable layouts. Through a case study on a 145k-document adversarial health communication dataset, we show that each edge-cutting strategy has advantages and disadvantages, and that the appropriate choice of strategy depends on the data, user, and task.},
	url = {https://doi.org/10.1109/BigData.2018.8622521},
	author = {Darren Edge and Jonathan Larson and Markus Mobius and Christopher White}
}
