Spring 2013 Talk Series on

Networks and Complex Systems

Every Monday 6-7p, Wells Library 001 ~ Optional Dinner at at Lennie's Afterwards

Description
This talk series is open to all Indiana University faculty and students interested in network analysis, modeling, visualization, and complex systems research. A major intent is to cross-fertilize between research done in the social and behavioral sciences, research in natural sciences such as biology or physics, but also research on Internet technologies.

Organizer
Katy Börner <katy@indiana.edu> Victor H. Yngve Professor of Information Science, Cyberinfrastructure for Network Science Center, SLIS, IUB.

Time & Place
Every Monday 6:00-7:00pm in Room LI001, Wells Library (formerly Main Library) at Indiana University, Bloomington. Right after the Cognitive Science Colloquium Series. We frequently go out for dinner 7:00-9:00pm at Lennie's and you are welcome to join.

Previous Talks
Fall 2004 | Spring 2005 | Fall 2005 | Spring 2006 | Fall 2006 | Spring 2007 | Fall 2007 | Spring 2008 | Fall 2008 | Spring 2009 | Fall 2009 | Spring 2010 | Fall 2010 | Spring 2011 | Fall 2011 | Fall 2012

Networks and Complex Systems Centers at Indiana University
Links to people, projects, groups, students, courses and news related to complex systems and networks research at Indiana University are linked from http://vivo-netsci.slis.indiana.edu. Major centers comprise:

Acknowledgement
This talk series is sponsored by the Cyberinfrastructure for Network Science Center and the School of Library and Information Science.

01/28 ChengXiang Zhai, Computer Science, University of Illinois at Urbana-Champaign (Hosted by Ying Ding) Rescheduled for Feb 11, 2013, 4-5p in Wells Library 031

materials iconmaterials iconAutomatic Construction of Topic Maps for Navigation in Information Space

Abstract: Querying and browsing are two complementary ways of finding relevant information items in an information space. Querying works well when a user has a clear goal of information seeking and knows how to formulate an effective query, whereas browsing is more useful when a user has a vague (exploratory) information need, or when a user cannot easily formulate an effecive query. Although users can freely query any information space by using a search engine, they only have very limited support for browsing, which currently can only be done based on manually generated links or category hierarchies. In order to freely browse an information space, users would need a more comprehensive topic map that can connect and organize all the information items in a meaningful way. Unfortunately, manual construction of such a topic map is labor-intensive and thus does not scale up well. In this talk, I will present two case studies of automatically constructing a topic map to enable effective browsing. In the first, we propose to view the search log data naturally available for an operational search system as the information footprints left by users and organize search logs into a multiresolution topic map, which enables a user to follw the footprints left by previous users to explore information flexibly. As new users use the map for navigation, they leave more footprints, which can then be used to enrich and refine the map dynamically and continuously for the benefit of future users, leading to a sustainable infrastructure to facilitate users to surf the information space in a collaborative manner. In the second, we develop probabilistic generative models to extract topics in text collections and connect them into various topic maps that can represent the information space from different perspectives. Such maps reveal interesting topic patterns buried in the text data and enable users to flexibly follow topic patterns and navigate into detailed information about each topic. In the end of the talk, I will discuss some challenges to be solved in order to seamlessly integrate querying and browsing to support multi-mode information seeking and analysis.

Bio: ChengXiang Zhai is an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign, where he also holds a joint appointment at the Graduate School of Library and Information Science, Institute for Genomic Biology, and Department of Statistics. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. He worked at Clairvoyance Corp. as a Research Scientist and a Senior Research Scientist from 1997 to 2000. His research interests include information retrieval, text mining, natural language processing, machine learning, and biomedical informatics. He is an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and serves on the editorial board of Information Retrieval Journal. He is a program co-chair of ACM CIKM 2004, NAACL HLT 2007, and ACM SIGIR 2009. He is an ACM Distinguished Scientist and the recipient of multiple best paper awards, UIUC Rose Award for Teaching Excellence, an Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Program Award, and Presidential Early Career Award for Scientists and Engineers (PECASE).

2/4 Ted Polley, Cyberinfrastructure for Network Science Center, SLIS, IUB.

materials iconmaterials iconTopical Analysis and Visualization of (Network) Data using Sci2

Abstract: This hands-on session introduces topical analysis and visualization of network data. Specifically, we will use the Sci2 tool to extract co-word occurence networks and to generate science map overlays.

Bio: Ted Polley is a Research Assistant at the Cyberinfrastructure for Network Science (CNS) Center. He recently obtained a dual Master’s degree in Library and Information Science from the School of Library and Information Science at Indiana University. He has extensive experience testing and documenting the information visualization software programs developed at CNS and serves as a professional contributor to research, performing software testing and documentation, and responding to user questions.

Please register for:

02/11Emilio Ferrara, SOIC, IUB

materials iconmaterials iconThe Digital Evolution of Occupy Wall Street

Abstract: The adoption of online social media to ease communication related to politics, policy and social protest has recently emerged as a prominent social phenomenon. Online social media have played important roles in social and political upheaval, such as the 2009 Arab Spring. By analyzing a high-volume, fifteen-month long dataset captured from Twitter, we provide a quantitative perspective on the birth and evolution of the US anti-capitalist movement known as Occupy Wall Street. Our analysis inspects individuals engagement, patterns of activity and social connectivity to investigate changes in online user behavior looking at participant interests and relations before, during and after the Occupy movement. In this talk I will show that Occupy has elicited participation mostly of users with pre-existing interests in domestic politics and foreign social movements. Occupy went through a short initial "explosive" phase, with high peaks of activity, and a dramatic decrease of volume shortly after. Online activity was strongly correlated with "on the ground" events, focused on organizational aspects more than collective framing. After the "high-activity" phase of the movement, we observe that user inter-connectivity and interests have remained mostly unchanged.

Bio: Emilio Ferrara is a Post-doctoral fellow at the School of Informatics and Computing of IU Bloomington. He works with A. Flammini and F. Menczer on a DARPA research project aiming at the detection of political abuse on social media. He received a PhD in Mathematics and Computer Science from University of Messina, Italy in 2012. During 2010 he was a visiting scholar at the Database and Artificial Intelligence group of Vienna University of Technology and an intern at Lixto GmbH; during 2011 and 2012 he was a visiting researcher at the Centre for Systems and Synthetic Biology of the Royal Holloway University of London. His research interests include social network and media analysis, knowledge engineering, machine learning, bio-informatics and algorithms. He has co-authored over 30 papers, appeared in PLoS One, Information Sciences, Knowledge-based Systems, Journal of Computer and System Sciences, EPJ Data Science, CIKM, and other prestigious venues.

2/18 Ted Polley, Cyberinfrastructure for Network Science Center, SLIS, IUB.

materials iconmaterials iconAdvanced Network Analysis and Visualization: Hierarchical Networks using Sci2 and OSLOM

Abstract: This hands-on session introduces the Blondel community detection algorithm and the circular hierarchy network visualization together with the multifunctional algorithm package OSLOM (www.oslom.org) that handles edge directions, edge weights, overlapping communities and hierarchies.

Bio: Ted Polley is a Research Assistant at the Cyberinfrastructure for Network Science (CNS) Center. He recently obtained a dual Master’s degree in Library and Information Science from the School of Library and Information Science at Indiana University. He has extensive experience testing and documenting the information visualization software programs developed at CNS and serves as a professional contributor to research, performing software testing and documentation, and responding to user questions.

Please register for:

02/25 Matthew Hampton, Senior Geodesigner, Oregonmetro

materials iconmaterials iconAtlas of a regional transportation network: Oregon Metro's Mobility Corridor Atlas ~ Draft 1.0

Abstract: Regional transportation networks move goods, services and people across multiple city, village and neighborhood scales using a variety of modes (auto, transit, bike, pedestrian, etc.). Capturing, modeling and displaying this data for planning purposes in the Portland, Oregon metropolitan region resulted in Metro's first Mobility Corridor Atlas. Mobility Corridors are a new way to organize, integrate and understand land use and transportation data. This concept focuses on the regionís network of freeways and highways and includes parallel networks of arterial streets, regional multi-use paths, high capacity transit and frequent bus service. The function of this network of integrated transportation corridors is metropolitan mobility and in some corridors, connecting the region with the rest of the state and beyond. Visual techniques to depict land use, traffic flow, volume, speed, capacity, and direction are explored to help policymakers develop strategies that improve mobility in 24 corridors. Also learn how regional bike and pedestrian networks are analyzed to display variations of connectivity, density, permeability, land use, topography, safety, sidewalk completion, tree canopy and potential for version 2.0.

Bio: Matthew Hampton has 15 years of progressive work experience in design cartography and applied geospatial analysis. Born and raised in western Montana, Matthew studied the epistemology of science at Lewis & Clark College in Portland, Oregon and graduated with a degree in Social and Cultural Anthropology. After working as a wilderness guide in the Pacific Northwest, he finished a Master's degree in Geography at Portland State University and joined the Transportation Planning Department at Metro. As a Senior Geodesigner at Metro, Matthew provides analysis, creates maps, visualizations and infographics of transportation networks to guide the region's growth and development. He currently serves on the board of the North American Cartographic Information Society (NACIS).

03/04 Ann McCranie, Department of Sociology, IUB

materials iconmaterials iconReframing Recovery: Disciplinary Boundary Maintenance in Mental Health Services

Abstract: The concept of recovery in mental health services research refers broadly to the idea that individuals with serious mental illness can get better. This may not seem a revolutionary idea on its face, but for the mental health services research field, it has proven a potent scientific/intellectual movement that has influenced treatment and outcomes research and even policy development over the past 20 years. However, the idea of recovery, which had its origins in "consumer" criticism of the prevailing psychiatric understanding of illnesses such as schizophrenia and bipolar disorder, has no clear and agreed-upon definition within the research field. In the absence of agreement and clarity, the various disciplines and approaches to serious mental illness (such as biomedical psychiatry, psychosocial rehabilitation, and peer services) each have adopted significantly different approaches to what it means to be "recovery-oriented." Using main path and positional analysis, this study investigates the most influential works in the field of recovery research and traces the development since 1993, documenting the ways in which the field of psychiatry has managed to reframe the issue of recovery from its implicit critique of the profession into a new field of "remission" studies.

Bio: Before arriving at Indiana University, I spent several years as a community journalist in the foothills of the Appalachians. As a reporter, one issue among many that I covered was the transformation of the community mental health center model of treatment in North Carolina. This led directly to a research interest in the organization of care and the social integration of individuals living with severe mental illness. Since arriving at IUB's sociology department and working with my mentor and dissertation chair, Bernice Pescosolido, my interests have continued to focus on serious mental illness, but have also expanded into social networks and the sociology of organizations. My dissertation is about the scientific/intellectual movement of "recovery" in mental health services research over the last 20 year in the United States. I take an explicitly networks-driven approach to studying the community of researchers that has emerged around this topic. I am currently the managing editor of a new interdisciplinary journal, Network Science, published by Cambridge University Press and scheduled to begin publication in April 2013. I teach a number of workshops and courses on network analysis, primarily through the ICPSR Summer Program in Quantitative Methods of Social Research.

03/11Spring Break

03/25 Exploiting Big Data Semantics for Translational Medicine talks, IUB. (Ying Ding, David Wild, Eric Gifford, Katy Borner host)

materials icon

Indiana Memorial Union Dogwood Room, 3pm-5pm

Indiana University School of Library and Information Science and School of Informatics and Computing will be hosting a workshop entitled Exploiting Big Data Semantics in Translational Medicine. This workshop will bring together leading practitioners from around the world in the areas of semantic technologies, network science and visualization, and computational translational medicine to identify the most critical areas for collaboration between these fields to maximize impact on the next generation of disease treatments. As part of the workshop, there will be three short overview talks open to the public, covering aspects of these fields.

New opportunities for biomedical science and drug discovery using semantic technologies
Erik Schultes, Leiden University, and David Wild, Indiana University

Driving translational medicine through big data and ontologies
Mark Musen and Nigam Shah, Stanford University

Open Code and Open Education: The Information VIsualization MOOC
Katy Borner, Indiana University

04/01 Daniel G. Aliaga, Computer Science, Purdue University

materials iconmaterials iconCities of Tomorrow: Visual Computing for Sustainable Urban Ecosystems

Abstract: Cities are extremely complex ecosystems of human activities for living, working, and entertainment its many inhabitants. In 1900, fourteen percent of the world’s 1.6 billion people lived in cities. Today, more than 50 percent of the world’s 7 billion people live in cities – and that number is only expected to grow over the next decades. Moreover, although cities only occupy two percent of the Earth’s surface, the concentrated presence of man-made structures, the lack of tools for rapidly exploring the effect of different city geometries, and the high-resource consumption severely affect the surrounding environment causing strong and unwanted consequences. In this talk, Dr. Aliaga will describe multi-disciplinary research at Purdue (www.cs.purdue.edu/cgvlab/urban) focused on interactive visual computing tools for improving the complex urban ecosystem and for “what-if” exploration of sustainable urban designs. The projects to be shown provide computing platforms to tightly integrate 3D urban modeling with urban simulation, visualization, meteorology, and vegetation modeling.

Bio: Dr. Daniel G. Aliaga’s research is primarily in the area of 3D computer graphics but overlaps with computer vision and with visualization. He focuses on i) 3D urban modeling (creating novel 3D urban acquisition algorithms, forward and inverse procedural modeling, and integration with urban design and planning), ii) projector-camera systems (focusing on algorithms for spatially-augmented reality and for appearance editing of arbitrarily shaped and colored objects), and iii) 3D digital fabrication (creating novel methods for digital manufacturing that embed into a physical object information for genuinity detection, tamper detection, and multiple appearance generation). Dr. Aliaga has also performed research in 3D reconstruction, image-based rendering, rendering acceleration, and camera design and calibration. To date Prof. Aliaga has published over 80 peer reviewed publications and chaired and served on numerous ACM and IEEE conference and workshop committees, including being a member of more than 40 program committees, conference chair, papers chair, invited speaker, and invited panelist. In addition, Dr. Aliaga has served on several NSF panels, is on the editorial board of Graphical Models, and is a member of ACM SIGGRAPH. His research has been whole or partially funded by NSF, MTC, Microsoft Research, Google, and Adobe Inc.

04/08 William Ribarsky, Chair of the Computer Science Department, Director of the Charlotte Visualization Center, College of Computing and Informatics, University of North Carolina at Charlotte

materials iconmaterials iconSocial Media Analysis as Social History

Abstract: Streaming social media are a prime example of information physicality (location in both space and time) and information sociality. Extension of automated analysis techniques, such as topic modeling and entity extraction, to textual content in these media over time have made these aspects available, organizable, and interpretable. This has resulted in an unprecedented capability to identify events and their aftermaths, infer relationships including potential causes and effects, and tell stories. In addition, related analyses can identify social networks, communities of interest, and how they evolve over time, as well as show how ideas are disseminated through networks and individuals. The result will be a new and much richer way to build social history than was available before, where history is defined in the broadest way to include not only past events but how the present evolves and what all this tells us about the future. In this talk, I will discuss some fundamental work we have done in this area and the applications that have resulted.

Bio: William Ribarsky is the Bank of America Endowed Chair in Information Technology at UNC Charlotte and the founding director of the Charlotte Visualization Center. He is currently Chair of the Computer Science Department. His research interests include visual analytics; 3D multimodal interaction; bioinformatics visualization; sustainable system analytics; visual reasoning; and interactive visualization of large-scale information spaces. Dr. Ribarsky is the former Chair and a current Director of the IEEE Visualization and Graphics Technical Committee. He is also a member of the overall Steering Committees for IEEE VisWeek, which comprises the Scientific Visualization, Information Visualization, and Visual Analytics Conferences, the leading international conferences in their respective fields. He was an Associate Editor of IEEE Transactions on Visualization and Computer Graphics and is currently an Editorial Board member for IEEE Computer Graphics & Applications. Dr. Ribarsky co-founded the Eurographics/IEEE visualization conference series (now called EG/IEEE EuroVis) and led the effort to establish the current Virtual Reality Conference series. For the above efforts on behalf of IEEE, Dr. Ribarsky won the IEEE Meritorious Service Award in 2004. In 2007, he was general co-chair of the IEEE Visual Analytics Science and Technology (VAST) Symposium. Dr. Ribarsky has published over 160 scholarly papers, book chapters, and books. He has received competitive research grants and contracts from NSF, ARL, ARO, DHS, NIH, ONR, EPA, AFOSR, DARPA, NASA, NIMA, US DOT, National Institute of Justice, and several companies.

04/15 William S. Cleveland, Statistics and Computer Science, Purdue University

materials iconmaterials iconDivide and Recombine for the Analysis of Large Complex Data

Abstract: Large complex data challenge all of the intellectual components of data science: statistical theory, statistical and machine learning methods, visualization methods, statistical models, computational algorithms, and computational environments. In meeting the challenge we set two goals that have been achieved for small data and that it is critical to preserve. The first is deep analysis: comprehensive analysis, including visualization of the detailed data at their finest granularity, which minimizes the risk of losing important information in the data. The second is a computational environment where an analyst programs exclusively with an interactive language for data analysis such as R, making programming with the data very efficient. D&R (datadr.org) is an approach that seeks to achieve these goals.  The data analyst divides the data into subsets and writes them to disk. Then analytic methods are applied to each subset. The analytics are statistical methods whose output is categorical or numeric, and visualization methods whose output is visual. An analytic method is applied to each subset independently of the other subset. Then the outputs of each method are recombined.  In our analyses of large complex data the number of subsets have varied from hundreds to millions. D&R computation is almost all embarrassingly parallel: no communication among parallel computations, which is the simplest parallel computation. This is exploited by RHIPE (R and Hadoop Integrated Processing Environment), which means "in a moment in Greek". It is a merger of R and Hadoop that allows a D&R analysis of a large complex dataset to be carried out wholly from within R. For statistical methods applied to the data, D&R estimators have different theoretical properties from the direct whole-data estimators, and are typically less efficient.  However, if the division is carried out using statistical thinking (e.g., stratified sampling) to make each subset as representative as possible, then results can be excellent. This is replicate division. In addition, division is also often guided by variables important to the analysis in which it is natural as an analysis strategy to break up the data according to their values. This is conditioning variable division. For visualization methods, the analyst applies a method to each of a number of subsets, typically not to all because there are far too many to view. Instead, statistical sampling is used to select a limited number. The subsets contain the detailed data, so this enables visualization of the data at its finest granularity. While the sampling is a data reduction method, it is a rigorous one that uses the same statistical thinking as survey sampling, but in D&R there is an advantage because all of the data are in hand, which can be exploited in developing a sampling plan.

Bio: William S. Cleveland is the Shanti S. Gupta Distinguished Professor of Statistics and Courtesy Professor of Computer Science at Purdue University. His areas of methodological research are in statistics, machine learning, and data visualization. He has analyzed data sets ranging from small to large and complex in his research in cyber security, computer networking, visual perception, environmental science, healthcare engineering, public opinion polling, and disease surveillance.  In the course of this work, Cleveland has developed many new methods and models for data that are widely used throughout the worldwide technical community. Cleveland has led teams developing software systems implementing his methods that have become core programs in many commercial and open-source systems. One example is the trellis display framework for data visualization that initially became a part of the commercial S-Plus environment, and was later incorporated into the R environment as lattice graphics by Deepayan Sarkar. In 1996 Cleveland was chosen national Statistician of the Year by the Chicago Chapter of the American Statistical Association.  He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association of the Advancement of Science, and the International Statistical Institute.  In 2002 he was selected as a Highly Cited Researcher by the American Society for Information Science & Technology in the newly formed mathematics category. Google Scholar shows 15,756 citations to his books and papers.

04/22 Divesh Srivastava, AT&T Labs-Research

materials iconmaterials iconIn Search of Truth (on the Deep Web)

Abstract: The Deep Web has enabled the availability of a huge amount of useful information and people have come to rely on it to fulfill their information needs in a variety of domains. We present a recent study on the accuracy of data and the quality of Deep Web sources in two domains where quality is important to people's lives: Stock and Flight. We observe that, even in these domains, the quality of the data is less than ideal, with sources providing conflicting, out-of-date and incomplete data. Sources also copy, reformat and modify data from other sources, making it difficult to discover the truth. We describe techniques proposed in the literature to solve these problems, evaluate their strengths on our data, and identify directions for future work in this area.

Bio: Divesh Srivastava is the head of the Database Research Department at AT&T Labs-Research. He received his Ph.D. from the University of Wisconsin, Madison, and his B.Tech from the Indian Institute of Technology, Bombay. He is a fellow of the ACM, and his research interests span a variety of topics in data management.

04/29 Alberto Cairo, University of Miami

materials iconmaterials iconThe Scientist and the Journalist: Storytelling with Data Visualization and Infographics

Abstract: This lecture builds bridges between the two cultures that coexist in visualization: that of graphic designers and journalists—interested in presenting concise summaries of data in an engaging way—and that of researchers and scientists—who strive for efficiency, functionality, and complexity. In the past, some experts claimed that these two cultures are irreconciliable, but the truth is that there's much to be gained from an open conversation between them.

Bio: Alberto Cairo is the author of The Functional Art: An Introduction to Information Graphics and Visualization (PeachPit Press, 2012). He teaches infographics and visualization at the School of Communication of the University of Miami since January 2012. Before that, he was director of infographics at Editora Globo, Brazil (2010-2011), James H. Schumaker Term Assistant Professor at UNC-Chapel Hill (2005-2009), and director of online infographics at El Mundo, Spain (2000-2005). His website is www.thefunctionalart.com

Fall 13 Yong-Yeol Ahn, SOIC, IUB

materials iconmaterials iconNetwork Community Analysis

Abstract: This hands-on session introduces the concept of network communities and explore several tools to detect communities. We will discuss how community analysis can be applied and the characteristics of various methods. We will play with multiple tools, both command-line tools and GUI tools to identify communities. 

Bio: Dr. Yong-Yeol Ahn is an assistant professor at Indiana University School of Informatics and Computing and a co-founder of Janys Analytics. He earned his Ph.D. from Physics Department at KAIST in early 2008 and was a postdoctoral researcher at the Center for Complex Network Research at Northeastern University and a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute from 2008 till 2011. He is interested in the structure and dynamics of complex systems, such as society and living organisms. 

Speakers in Fall 13 (not all are confirmed)

  1. Constantine Dovrolis, Georgia Tech (via Fil)
  2. Michael J. Pellegrino, Pellegrino and Associates (via Karl Koehler)
  3. Bernice Pescosolido, Sociology, IUB
  4. Noshir Contractor, Northwestern U
  5. Titus Schleyer, IUPUI
  6. Alan Murray, PewResearchCenter
  7. Caroline Wagner, OSU
  8. Merih Sevilit, Noah Stiffman, Scott Yonker, Kelley School of Bussiness, IUB (via Bernice)
  9. Jevin West (http://octavia.zoology.washington.edu/people/jevin/Homepage.html) (via Cassidy)
  10. Woody Powell, Stanford University -- The Emergence of Organizations and Markets
  11. Paulette Lloyd, Department of Sociology, IUB
  12. David S. Ebert, Purdue
  13. Chintan Tank, General Sentiment, New York, USA
  14. David Coe, Walker Information, Indianapolis
  15. Jim Crutchfield, UCDavis
  16. Marshall Scott Poole, National Center for Supercomputing Applications, and Director of The Institute for Computing in the Humanities, Arts, and Social Sciences, University of Illinois
  17. Scott Long, Sociology
  18. Bill Ribarsky, Computer Science Department, Director od the Charlotte Visualization Center
  19. Thom Hickey, OCLC
  20. Daniel Aliaga, Purdue U
  21. Peter Bearman, Columbia University
  22. James Fowler, UCSD
  23. Nicolas Christakis, Harvard Department of Sociology

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