TY - JOUR T1 - The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2019 A1 - Sun, Maoyuan A1 - Zhao, Jian A1 - Hao Wu A1 - Luther, Kurt A1 - North, Chris A1 - Ramakrishnan, Naren KW - Bicluster KW - bicluster visualizations KW - bicluster-based seriation KW - Bioinformatics KW - Bipartite graph KW - bipartite graph based visualizations KW - data analysis KW - data visualisation KW - edge bundles KW - edge bundling KW - edge crossings KW - exploratory data analysis KW - graph theory KW - Image edge detection KW - Layout KW - pattern clustering KW - product bundles KW - seriation KW - Visual Analytics AB - 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. VL - 25 IS - 10 ER - TY - CONF T1 - Interactive Bicluster Aggregation in Bipartite Graphs T2 - VIS 2019 Short Papers Y1 - 2019 A1 - Sun, Maoyuan A1 - Koop, David A1 - Zhao, Jian A1 - North, Chris A1 - Ramakrishnan, Naren JF - VIS 2019 Short Papers ER - TY - JOUR T1 - Be the Data: Embodied Visual Analytics JF - IEEE Transactions on Learning Technologies Y1 - 2018 A1 - Xin Chen A1 - Self, Jessica Zeitz A1 - House, Leanna A1 - Wenskovitch, John A1 - Sun, Maoyuan A1 - Nathan Wycoff A1 - Jane Robertson Evia A1 - Leman, Scotland A1 - North, Chris VL - 11 IS - 1 ER - TY - JOUR T1 - Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models JF - ACM Transactions on Knowledge Discovery from Data Y1 - 2018 A1 - Hao Wu A1 - Sun, Maoyuan A1 - Peng Mi A1 - Nikolaj Ta A1 - North, Chris A1 - Ramakrishnan, Naren VL - 12 IS - 1 ER - TY - JOUR T1 - AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets JF - Informatics Y1 - 2016 A1 - Peng Mi A1 - Sun, Maoyuan A1 - Moeti Masiane A1 - Yong Cao A1 - North, Chris PB - MDPI VL - 3 UR - http://www.mdpi.com/2227-9709/3/4/18 IS - 4 ER - TY - CONF T1 - Be the Data: Social Meetings with Visual Analytics T2 - International Workshop on Visualization and Collaboration (VisualCol 2016) Y1 - 2016 A1 - Xin Chen A1 - Self, Jessica Zeitz A1 - Sun, Maoyuan A1 - House, Leanna A1 - North, Chris JF - International Workshop on Visualization and Collaboration (VisualCol 2016) ER - TY - JOUR T1 - BiSet: Semantic Edge Bundling with Biclusters for Sensemaking JF - Visualization and Computer Graphics, IEEE Transactions on Y1 - 2016 A1 - Sun, Maoyuan A1 - Peng, Mi A1 - North, Chris A1 - Ramakrishnan, Naren AB - 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. PB - IEEE VL - 22 IS - 1 ER - TY - JOUR T1 - Interactive Graph Layout of a Million Nodes JF - Informatics Y1 - 2016 A1 - Peng Mi A1 - Sun, Maoyuan A1 - Moeti Masiane A1 - Yong Cao A1 - North, Chris VL - 3 UR - http://www.mdpi.com/2227-9709/3/4/23 IS - 4 ER - TY - CONF T1 - Usability Challenges underlying Bicluster Interaction for Sensemaking T2 - CHI 2016 Workshop on Human Centred Machine Learning Y1 - 2016 A1 - Sun, Maoyuan A1 - Peng Mi A1 - Hao Wu A1 - North, Chris A1 - Ramakrishnan, Naren JF - CHI 2016 Workshop on Human Centred Machine Learning ER - TY - CONF T1 - Visualizing Traffic Causality for Analyzing Network Anomalies T2 - Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics Y1 - 2015 A1 - Zhang, Hao A1 - Sun, Maoyuan A1 - Yao, Danfeng A1 - North, Chris KW - anomaly detection KW - information visualization KW - network traffic analysis KW - usable security KW - visual locality JF - Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics T3 - IWSPA '15 PB - ACM CY - New York, NY, USA SN - 978-1-4503-3341-2 UR - http://doi.acm.org/10.1145/2713579.2713583 ER - TY - JOUR T1 - A Five-Level Design Framework for Bicluster Visualizations JF - Visualization and Computer Graphics, IEEE Transactions on Y1 - 2014 A1 - Sun, Maoyuan A1 - North, C. A1 - Ramakrishnan, N. KW - bicluster visualizations KW - Biclusters KW - Bioinformatics KW - Cluster approximation KW - coordinated relationships KW - data analysis KW - Data mining KW - data visualisation KW - design framework KW - five-level design framework KW - interactive visual analytics KW - navigation KW - pattern clustering KW - Visual Analytics KW - visual analytics tools AB - 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. VL - 20 IS - 12 ER - TY - CONF T1 - The Role of Interactive Biclusters in Sensemaking T2 - Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems Y1 - 2014 A1 - Sun, Maoyuan A1 - Lauren Bradel A1 - North, Chris L. A1 - Ramakrishnan, Naren KW - biclustering KW - Intelligence Analysis KW - visual interaction JF - Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems T3 - CHI '14 PB - ACM CY - New York, NY, USA SN - 978-1-4503-2473-1 UR - http://doi.acm.org/10.1145/2556288.2557337 ER - TY - JOUR T1 - Bixplorer: Visual Analytics with Biclusters JF - Computer Y1 - 2013 A1 - Fiaux, Patrick A1 - Sun, Maoyuan A1 - Lauren Bradel A1 - North, Chris A1 - Ramakrishnan, Naren A1 - Endert, Alex VL - 46 IS - 8 JO - Computer ER -