Egocentric storylines for visual analysis of large dynamic graphs

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.

[1]  Gautam Kumar,et al.  Visual Exploration of Complex Time-Varying Graphs , 2006, IEEE Transactions on Visualization and Computer Graphics.

[2]  Andreas Noack,et al.  Modularity clustering is force-directed layout , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Kwan-Liu Ma,et al.  Visual Recommendations for Network Navigation , 2011, Comput. Graph. Forum.

[4]  U. Brandes,et al.  Maximizing Modularity is hard , 2006, physics/0608255.

[5]  Stephen C. North,et al.  Incremental Layout in DynaDAG , 1995, GD.

[6]  Andrew E. Johnson,et al.  Visualizing the Evolution of Community Structures in Dynamic Social Networks , 2011, Comput. Graph. Forum.

[7]  Jeffrey Heer,et al.  Tracing genealogical data with TimeNets , 2010, AVI.

[8]  Kwan-Liu Ma,et al.  Design Considerations for Optimizing Storyline Visualizations , 2012, IEEE Transactions on Visualization and Computer Graphics.

[9]  Jean-Daniel Fekete,et al.  Author Manuscript, Published in "sigchi Conference on Human Factors in Computing Systems Topology-aware Navigation in Large Networks , 2022 .

[10]  Helen C. Purchase,et al.  Extremes Are Better: Investigating Mental Map Preservation in Dynamic Graphs , 2008, Diagrams.

[11]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[12]  Michael Burch,et al.  Parallel Edge Splatting for Scalable Dynamic Graph Visualization , 2011, IEEE Transactions on Visualization and Computer Graphics.

[13]  Ayellet Tal,et al.  Online Dynamic Graph Drawing , 2008, IEEE Transactions on Visualization and Computer Graphics.

[14]  G. W. Furnas,et al.  Generalized fisheye views , 1986, CHI '86.

[15]  Kwan-Liu Ma,et al.  Software evolution storylines , 2010, SOFTVIS '10.

[16]  Stephan Diehl,et al.  Dynamic Graph Drawing of Sequences of Orthogonal and Hierarchical Graphs , 2004, GD.

[17]  Kwan-Liu Ma,et al.  Clustering, Visualizing, and Navigating for Large Dynamic Graphs , 2012, GD.

[18]  Tamara Munzner,et al.  Visual Exploration of Complex Time-Varying Graphs , 2006 .

[19]  Kwan-Liu Ma,et al.  Visualizing social interaction in open source software projects , 2007, 2007 6th International Asia-Pacific Symposium on Visualization.

[20]  Charu C. Aggarwal,et al.  Graph Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.

[21]  Satu Elisa Schaeffer,et al.  Graph Clustering , 2017, Encyclopedia of Machine Learning and Data Mining.

[22]  Xin Tong,et al.  TextFlow: Towards Better Understanding of Evolving Topics in Text , 2011, IEEE Transactions on Visualization and Computer Graphics.

[23]  Stephan Diehl,et al.  Graphs, They Are Changing , 2002, GD.

[24]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[25]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[26]  Jean-Daniel Fekete,et al.  Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines , 2010, IEEE Transactions on Visualization and Computer Graphics.

[27]  Ulrik Brandes,et al.  A Quantitative Comparison of Stress-Minimization Approaches for Offline Dynamic Graph Drawing , 2011, GD.

[28]  Stephen G. Kobourov,et al.  GraphAEL: Graph Animations with Evolving Layouts , 2003, GD.

[29]  Kwan-Liu Ma,et al.  Visual Reasoning about Social Networks Using Centrality Sensitivity , 2012, IEEE Transactions on Visualization and Computer Graphics.

[30]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[31]  Helen C. Purchase,et al.  The 'Mental Map' versus 'Static Aesthetic' Compromise in Dynamic Graphs : A User Study , 2008, AUIC.

[32]  Eve E. Hoggan,et al.  How Important Is the "Mental Map"? - An Empirical Investigation of a Dynamic Graph Layout Algorithm , 2006, GD.

[33]  Ulrik Brandes,et al.  Visualizing Internet Evolution on the Autonomous Systems Level , 2007, GD.

[34]  Frank van Ham,et al.  “Search, Show Context, Expand on Demand”: Supporting Large Graph Exploration with Degree-of-Interest , 2009, IEEE Transactions on Visualization and Computer Graphics.

[35]  Daniel W. Archambault,et al.  Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs , 2011, IEEE Transactions on Visualization and Computer Graphics.

[36]  Yifan Hu,et al.  Embedding, clustering and coloring for dynamic maps , 2012, 2012 IEEE Pacific Visualization Symposium.