@article {DOI10.1109/TVCG.2018.2861397, title = {The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs}, journal = {IEEE Transactions on Visualization and Computer Graphics}, volume = {25}, year = {2019}, month = {07/2019}, pages = {2983-2998}, abstract = {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. }, keywords = {Bicluster, bicluster visualizations, bicluster-based seriation, Bioinformatics, Bipartite graph, bipartite graph based visualizations, data analysis, data visualisation, edge bundles, edge bundling, edge crossings, exploratory data analysis, graph theory, Image edge detection, Layout, pattern clustering, product bundles, seriation, Visual Analytics}, doi = {10.1109/TVCG.2018.2861397}, author = {Sun, Maoyuan and Zhao, Jian and Hao Wu and Luther, Kurt and North, Chris and Ramakrishnan, Naren} } @conference {DOI300, title = {Interactive Bicluster Aggregation in Bipartite Graphs}, booktitle = {VIS 2019 Short Papers}, year = {2019}, month = {10/2019}, author = {Sun, Maoyuan and Koop, David and Zhao, Jian and North, Chris and Ramakrishnan, Naren} } @article {DOI269, title = {Be the Data: Embodied Visual Analytics}, journal = {IEEE Transactions on Learning Technologies}, volume = {11}, year = {2018}, pages = {81-95}, doi = {10.1109/TLT.2017.2757481}, author = {Xin Chen and Self, Jessica Zeitz and House, Leanna and Wenskovitch, John and Sun, Maoyuan and Nathan Wycoff and Jane Robertson Evia and Leman, Scotland and North, Chris} } @article {DOI261, title = {Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models}, journal = {ACM Transactions on Knowledge Discovery from Data}, volume = {12}, number = {7}, year = {2018}, month = {02/2018}, doi = {10.1145/3047017}, author = {Hao Wu and Sun, Maoyuan and Peng Mi and Nikolaj Ta and North, Chris and Ramakrishnan, Naren} } @article {DOI252, title = {AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets}, journal = {Informatics}, volume = {3}, year = {2016}, month = {10/2016}, pages = {18}, publisher = {MDPI}, edition = {Special Issue on Information Visualization for Massive Data}, doi = {10.3390/informatics3040018}, url = {http://www.mdpi.com/2227-9709/3/4/18}, author = {Peng Mi and Sun, Maoyuan and Moeti Masiane and Yong Cao and North, Chris} } @conference {DOI248, title = {Be the Data: Social Meetings with Visual Analytics}, booktitle = {International Workshop on Visualization and Collaboration (VisualCol 2016)}, year = {2016}, month = {11/2016}, pages = {8}, author = {Xin Chen and Self, Jessica Zeitz and Sun, Maoyuan and House, Leanna and North, Chris} } @article {DOI10.1109/TVCG.2015.2467813, title = {BiSet: Semantic Edge Bundling with Biclusters for Sensemaking}, journal = {Visualization and Computer Graphics, IEEE Transactions on}, volume = {22}, year = {2016}, month = {01/2016}, pages = {310-319}, publisher = {IEEE}, abstract = {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, {\textquotedblleft}in-between{\textquotedblright}, 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.}, issn = {1077-2626}, doi = {10.1109/TVCG.2015.2467813}, author = {Sun, Maoyuan and Peng, Mi and North, Chris and Ramakrishnan, Naren} } @article {DOI10.3390/informatics3040023 , title = {Interactive Graph Layout of a Million Nodes}, journal = {Informatics}, volume = {3}, year = {2016}, month = {12/2016}, pages = {23}, edition = {Special Issue on Information Visualization for Massive Data}, doi = {10.3390/informatics3040023 }, url = {http://www.mdpi.com/2227-9709/3/4/23}, author = {Peng Mi and Sun, Maoyuan and Moeti Masiane and Yong Cao and North, Chris} } @conference {DOI246, title = {Usability Challenges underlying Bicluster Interaction for Sensemaking}, booktitle = {CHI 2016 Workshop on Human Centred Machine Learning}, year = {2016}, month = {05/2016}, pages = {6 pages}, author = {Sun, Maoyuan and Peng Mi and Hao Wu and North, Chris and Ramakrishnan, Naren} } @conference {Zhang:2015:VTC:2713579.2713583, title = {Visualizing Traffic Causality for Analyzing Network Anomalies}, booktitle = {Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics}, series = {IWSPA {\textquoteright}15}, year = {2015}, pages = {37{\textendash}42}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {anomaly detection, information visualization, network traffic analysis, usable security, visual locality}, isbn = {978-1-4503-3341-2}, doi = {10.1145/2713579.2713583}, url = {http://doi.acm.org/10.1145/2713579.2713583}, author = {Zhang, Hao and Sun, Maoyuan and Yao, Danfeng and North, Chris} } @article {6875974, title = {A Five-Level Design Framework for Bicluster Visualizations}, journal = {Visualization and Computer Graphics, IEEE Transactions on}, volume = {20}, number = {12}, year = {2014}, month = {Dec}, pages = {1713-1722}, abstract = {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.}, keywords = {bicluster visualizations, Biclusters, Bioinformatics, Cluster approximation, coordinated relationships, data analysis, Data mining, data visualisation, design framework, five-level design framework, interactive visual analytics, navigation, pattern clustering, Visual Analytics, visual analytics tools}, issn = {1077-2626}, doi = {10.1109/TVCG.2014.2346665}, author = {Sun, Maoyuan and North, C. and Ramakrishnan, N.} } @conference {Sun:2014:RIB:2611528.2557337, title = {The Role of Interactive Biclusters in Sensemaking}, booktitle = {Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems}, series = {CHI {\textquoteright}14}, year = {2014}, pages = {1559{\textendash}1562}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {biclustering, Intelligence Analysis, visual interaction}, isbn = {978-1-4503-2473-1}, doi = {10.1145/2556288.2557337}, url = {http://doi.acm.org/10.1145/2556288.2557337}, author = {Sun, Maoyuan and Lauren Bradel and North, Chris L. and Ramakrishnan, Naren} } @article {DOI10.1109/MC.2013.269, title = {Bixplorer: Visual Analytics with Biclusters}, journal = {Computer}, volume = {46}, year = {2013}, month = {08/2013}, pages = {90 - 94}, issn = {0018-9162}, doi = {10.1109/MC.2013.269}, author = {Fiaux, Patrick and Sun, Maoyuan and Lauren Bradel and North, Chris and Ramakrishnan, Naren and Endert, Alex} }