IEEE EGAS 2015 will explore the technical challenges and technology development opportunities of graph visual analytics found in the big data era with the goal of solving big problems with big graph data. Today’s graph problems are increasingly multifaceted and multidisciplinary in nature. Many cutting-edge R&D efforts are conducted independently in disparate domains such as bioinformatics, cybersecurity, and predictive machine learning. Although technology transfers in big graph visualization and visual analytics are recognized and growing, little progress has been made in establishing a community strategy for sharing and building knowledge.
We invite researchers and practitioners with different interests to participate at IEEE EGAS 2015 by submitting technical or position papers and, if accepted, presenting their ideas at the workshop co-located at IEEE VIS 2015. A technical paper aims to discuss preliminary but promising results, whereas a position paper is meant to share visionary ideas and/or unsolved challenges that the paper authors have encountered.
We agree that the data size that seems big today is different from what seemed big only a few years ago. While the workshop doesn’t specify upper or lower bounds on the graph’s size, we are particularly interested in emerging problems that challenge conventional wisdom in computation and interaction brought by the latest social-scale or web-scale graphs.
Some of these pressing challenges are:
P Burkhardt and C Waring, An NSA Big Graph Experiment, Technical Report NSA-RD-2013-056002v1, U.S. National Security Agency, May 20, 2013.
P Burkhardt, “Big Graphs,” The Next Wave, 20(4), 2014.
VG Castellana, A Morari, J Weaver, A Tumeo, D Haglin, O Ville, and J Feo, “In-Memory Graph Databases for Web-Scale Data,” IEEE Computer, 48(3):24-35, Mar 2015.
D Ediger, K Jiang, EJ Riedy, and DA Bader, “GraphCT: Multithreaded Algorithms for Massive Graph Analysis,” IEEE Transactions on Parallel & Distributed Systems, 2012.
R Rohrer, CL Paul, and B Nebesh, “Visual Analytics for Big Data,” The Next Wave, 20(4), 2014.
A Morari, VG Castellana, O Villa, A Tumeo, J Weaver, D Haglin, J Feo, and S Choudhury, “Scaling Semantic Graph Databases in Size and Performance,” IEEE Micro, 34(4):16-26, Jul 2014.
PC Wong, H-W Shen, C Chen, C Johnson, and R Ross, “Top Ten Challenges in Extreme-Scale Visual Analytics,” IEEE Computer Graphics and Applications, 32(4):63-67, IEEE CS Press, Jul 2012.
Friday, August 14, 2015
Friday, Sept 18, 2015
8:00am - Noon
Monday, Oct 26, 2015
IEEE EGAS 2015 solicits 3-to-4 page technical or position papers. Authors must follow the IEEE TVCG formatting guidelines and submit pdf files via EASYCHAIR. Submissions will be reviewed by 2-3 domain experts. Accepted papers will be presented at the workshop and posted online (further information TBA). Authors will maintain the copyright to their work, which allows them to submit to or present at a different publication venues in the future.
U.S. National Security Agency
Paul Burkhardt is a computer science researcher in the Research Directorate at NSA. He received his PhD from the University of Illinois at Urbana-Champaign. His current research interests are primarily focused on graph algorithms and Big Data analytics.
|8:30 - 8:35||Pak Chung Wong||Opening, introduction|
|8:35 - 9:15||Paul Burkhardt||Keynote: "Graph it! Vis it!" (Presentation)|
|9:15 - 9:30||Scott Langevin, David Jonker, David Giesbrecht and Michael Crouch||Multi-Scale Community Visualization of Massive Graph Data (Presentation)|
|9:30 - 9:45||Lorne Leonard, Kamesh Madduri and Chris Duffy||Graph-based Analysis for Large-scale Hydrological Modelling (Presentation)|
|9:45 - 10:00||Michael Burch||Visualizing Large Dynamic Digraphs (Presentation)|
|10:00 - 10:05||Dongyu Liu, Fangzhou Guo, Bowen Deng, Yingcai Wu and Huamin Qu||egoComp: A Node-link Based Technique for Visual Comparison of Ego-network (Presentation)|
|10:05 - 10:10||Guohao Zhang, Peter Kochunov, Elliot Hong, Keqin Wu, Hamish Carr and Jian Chen||A Semantic Contour Tree Approach for Visual Comparison of Brain White Matter Connectivities in Cohorts|
|10:10 - 10:30||Coffee Break||Coffee Break|
|10:30 - 10:45||William Longabaugh||Using Linear Visualization to Explore Large Graphs (Presentation)|
|10:45 - 11:00||Joachim Giesen, Philipp Lucas, Claudia Dahl, Klaus Mueller and Shenghui Cheng||Exploring the Distribution of Local Neighborhood Structures in Large Networks (Presentation)|
|11:00 - 11:15||Alexander Garbarino, Zachary Garbarino, Liang Sun, Carl Schmidt and Jian Chen||VisGumbo, VisMirror, VisCut: Interactive Narrative Strategies for Large Biological Pathway Comparisons|
|11:15 - 11:30||Keqin Wu, Liang Sun, Carl Schmidt and Jian Chen||A Graph Query Algebra for Biological Pathway Exploration|
|11:30 - 11:45||Fangyan Zhang, Song Zhang, Pak Chung Wong, J. Edward Swan and T.J. Jankun-Kelly||A Visual and Statistical Benchmark for Graph Sampling Methods (Presentation)|
|11:45 - 12:10||David Haglin||Townhall meeting, closing|
Pacific Northwest National Laboratory
Pak Chung Wong is a chief scientist and project manager at PNNL. He has spent the last decade delivering working solutions to big-graph problems in areas such as social networks, telecommunication networks, cyber networks, and power grids. More recently, his research has focused on extreme-scale visual analytics as applied to exa-scale scientific datasets and web-scale graphs with up to a trillion edges. Wong serves as an Associate Editor-in-Chief of IEEE Computer Graphics and Applications and an Associate Editor of Information Visualization. In the past, he chaired IEEE VisWeek, IEEE InfoVis, IEEE VAST, and SPIE VDA conferences.
Pacific Northwest National Laboratory
David Haglin is the chief scientist for High Performance Data Analytics at PNNL where he leads a team of researchers working on large, sparse, irregular computational problems that are typically best characterized as graph computations. One of the key contributions of this program is a software ecosystem called Graph Engine for Multithreaded Systems (GEMS). This software supports the flexible attributed multi-graph data model. His earlier work focused on graph algorithms, machine learning, and high-performance computing. He has served on the program committee for IEEE IPDPS and is recognized as a Senior Member of IEEE and a Senior Member of ACM.
Georgia Institute of Technology
David A. Bader is a Full Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology, and Executive Director of High Performance Computing. His interests are at the intersection of high-performance computing and real-world applications, including computational biology and genomics and massive-scale data analytics. He is a Fellow of the IEEE and AAAS, a NSF CAREER Award recipient, and a co-founder of the Graph500 List for benchmarking “Big Data” computing platforms. Bader is recognized as a "RockStar" of High Performance Computing by InsideHPC and as HPCwire's People to Watch in 2012 and 2014.
U.S. Department of Defense
David Trimm received his PhD from the University of Maryland in 2012. As a Department of Defense employee, Dr. Trimm has over twenty years of experience working the cybersecurity mission. In this role, Dr. Trimm researches advanced tradecraft to include visual analytics, graph algorithms, and machine learning techniques.