Egocentric storylines for visual analysis of large dynamic graphs

Chris W. Muelder, Tarik Crnovrsanin, Arnaud Sallaberry, Kwan-Liu Ma
2013 2013 IEEE International Conference on Big Data  
Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.
doi:10.1109/bigdata.2013.6691715 dblp:conf/bigdataconf/MuelderCSM13 fatcat:j3vuhy7brjeozizgloc2ycbdcu