%0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2019 %T The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs %A Sun, Maoyuan %A Zhao, Jian %A Hao Wu %A Luther, Kurt %A North, Chris %A Ramakrishnan, Naren %K Bicluster %K bicluster visualizations %K bicluster-based seriation %K Bioinformatics %K Bipartite graph %K bipartite graph based visualizations %K data analysis %K data visualisation %K edge bundles %K edge bundling %K edge crossings %K exploratory data analysis %K graph theory %K Image edge detection %K Layout %K pattern clustering %K product bundles %K seriation %K Visual Analytics %X Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences. %B IEEE Transactions on Visualization and Computer Graphics %V 25 %P 2983-2998 %8 07/2019 %N 10 %R 10.1109/TVCG.2018.2861397 %0 Conference Paper %B VIS 2019 Short Papers %D 2019 %T Interactive Bicluster Aggregation in Bipartite Graphs %A Sun, Maoyuan %A Koop, David %A Zhao, Jian %A North, Chris %A Ramakrishnan, Naren %B VIS 2019 Short Papers %8 10/2019 %0 Journal Article %J IEEE Transactions on Learning Technologies %D 2018 %T Be the Data: Embodied Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A Wenskovitch, John %A Sun, Maoyuan %A Nathan Wycoff %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B IEEE Transactions on Learning Technologies %V 11 %P 81-95 %N 1 %R 10.1109/TLT.2017.2757481 %0 Journal Article %J ACM Transactions on Knowledge Discovery from Data %D 2018 %T Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models %A Hao Wu %A Sun, Maoyuan %A Peng Mi %A Nikolaj Ta %A North, Chris %A Ramakrishnan, Naren %B ACM Transactions on Knowledge Discovery from Data %V 12 %8 02/2018 %N 1 %R 10.1145/3047017 %0 Journal Article %J Informatics %D 2016 %T AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets %A Peng Mi %A Sun, Maoyuan %A Moeti Masiane %A Yong Cao %A North, Chris %B Informatics %7 Special Issue on Information Visualization for Massive Data %I MDPI %V 3 %P 18 %8 10/2016 %U http://www.mdpi.com/2227-9709/3/4/18 %N 4 %R 10.3390/informatics3040018 %0 Conference Paper %B International Workshop on Visualization and Collaboration (VisualCol 2016) %D 2016 %T Be the Data: Social Meetings with Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A Sun, Maoyuan %A House, Leanna %A North, Chris %B International Workshop on Visualization and Collaboration (VisualCol 2016) %P 8 %8 11/2016 %0 Journal Article %J Visualization and Computer Graphics, IEEE Transactions on %D 2016 %T BiSet: Semantic Edge Bundling with Biclusters for Sensemaking %A Sun, Maoyuan %A Peng, Mi %A North, Chris %A Ramakrishnan, Naren %X Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics. %B Visualization and Computer Graphics, IEEE Transactions on %I IEEE %V 22 %P 310-319 %8 01/2016 %N 1 %R 10.1109/TVCG.2015.2467813 %0 Journal Article %J Informatics %D 2016 %T Interactive Graph Layout of a Million Nodes %A Peng Mi %A Sun, Maoyuan %A Moeti Masiane %A Yong Cao %A North, Chris %B Informatics %7 Special Issue on Information Visualization for Massive Data %V 3 %P 23 %8 12/2016 %U http://www.mdpi.com/2227-9709/3/4/23 %N 4 %R 10.3390/informatics3040023 %0 Conference Paper %B CHI 2016 Workshop on Human Centred Machine Learning %D 2016 %T Usability Challenges underlying Bicluster Interaction for Sensemaking %A Sun, Maoyuan %A Peng Mi %A Hao Wu %A North, Chris %A Ramakrishnan, Naren %B CHI 2016 Workshop on Human Centred Machine Learning %P 6 pages %8 05/2016 %0 Conference Paper %B Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics %D 2015 %T Visualizing Traffic Causality for Analyzing Network Anomalies %A Zhang, Hao %A Sun, Maoyuan %A Yao, Danfeng %A North, Chris %K anomaly detection %K information visualization %K network traffic analysis %K usable security %K visual locality %B Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics %S IWSPA '15 %I ACM %C New York, NY, USA %P 37–42 %@ 978-1-4503-3341-2 %U http://doi.acm.org/10.1145/2713579.2713583 %R 10.1145/2713579.2713583 %0 Journal Article %J Visualization and Computer Graphics, IEEE Transactions on %D 2014 %T A Five-Level Design Framework for Bicluster Visualizations %A Sun, Maoyuan %A North, C. %A Ramakrishnan, N. %K bicluster visualizations %K Biclusters %K Bioinformatics %K Cluster approximation %K coordinated relationships %K data analysis %K Data mining %K data visualisation %K design framework %K five-level design framework %K interactive visual analytics %K navigation %K pattern clustering %K Visual Analytics %K visual analytics tools %X Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools. %B Visualization and Computer Graphics, IEEE Transactions on %V 20 %P 1713-1722 %8 Dec %N 12 %R 10.1109/TVCG.2014.2346665 %0 Conference Paper %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %D 2014 %T The Role of Interactive Biclusters in Sensemaking %A Sun, Maoyuan %A Lauren Bradel %A North, Chris L. %A Ramakrishnan, Naren %K biclustering %K Intelligence Analysis %K visual interaction %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %S CHI '14 %I ACM %C New York, NY, USA %P 1559–1562 %@ 978-1-4503-2473-1 %U http://doi.acm.org/10.1145/2556288.2557337 %R 10.1145/2556288.2557337 %0 Journal Article %J Computer %D 2013 %T Bixplorer: Visual Analytics with Biclusters %A Fiaux, Patrick %A Sun, Maoyuan %A Lauren Bradel %A North, Chris %A Ramakrishnan, Naren %A Endert, Alex %B Computer %V 46 %P 90 - 94 %8 08/2013 %N 8 %! Computer %R 10.1109/MC.2013.269