<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Darren Edge</style></author><author><style face="normal" font="default" size="100%">Jonathan Larson</style></author><author><style face="normal" font="default" size="100%">Markus Mobius</style></author><author><style face="normal" font="default" size="100%">Christopher White</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trimming the Hairball: Edge Cutting Strategies for Making Dense Graphs Usable</style></title><secondary-title><style face="normal" font="default" size="100%">2018 IEEE International Conference on Big Data (Big Data)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1109/BigData.2018.8622521</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>