Speaker: Rossano Schifanella, University of Turin
Title: Mapping the Sensorial Layers of a City
Date: 01/29/2016
Time: 4pm
Room: Informatics East 130
Abstract: Researchers have used large quantities of online data to study dynamics in novel ways. Consider the specific case of online networked individuals (e.g., users of Twitter, Instagram, Flickr). Can their social dynamics be used to build better tools for future cities? To answer this question, our research has focused on understanding how people psychologically experience cities. As a result, we have created new mapping tools that leverage senses and emotions, thus complementing the corporate idea of an efficient and predictable smart city, with the ultimate goal of reaching urban happiness. The work presented in this talk mixes data mining, urban informatics, and computational social science to show how a creative use of social media and network-generated data can capture the aesthetic, olfactory and sonic layers of our cities at scale.

Biography: Rossano Schifanella is an Assistant Professor in Computer Science at the Department of Computer Science of the University of Turin, where he is a member of the Applied Research on Computational Complex Systems group. He is a former visitor and collaborator of the Network & agents Network (NaN) group at the Center for Complex Networks and Systems Research (CNetS), and he is also a Visiting Scientist at Yahoo Labs where he studies the behavior of (groups of) individuals and their interactions on social media platforms, and the interplay between online and offline social behavior. He is also interested in computational methods to investigate social phenomena, aesthetics and creativity in media platforms, and human-centered ways to explore urban scenarios.

Speaker: Mike Conover, LinkedIn
Title: Unleashing the Hidden Power of Gephi
Date: 10/06/2015
Time: 5:15 pm
Room: Informatics West 232
Abstract: Gephi is a familiar standard for exploring the structure of complex networks. Powerful though it is, much of its core functionality remains hidden behind non-obvious user interface elements. In this tutorial session, I’ll review some advanced techniques for analyzing and visualizing networks using the Gephi platform, including:

• Multi-node Brushing, Dragging & Grouping
• Numeric Range & Categorical Attribute Filtering
• Attribute-based Color & Sizing
• Performance Speedups
• Interactive Analysis
• Workspaces & Subgraphs
• Working with Data Laboratory
• Essential Plugins
• Layout Parameterization

Feel free to bring laptops loaded with your favorite datasets to hack on during the demo. Ahead of the session, you might want to review the latest materials on OS X / Gephi compatibility [1,2], as there are some JVM issues that can complicate launching Gephi on recent versions OS X.
Biography: Mike Conover is a data scientist at LinkedIn and a graduate of the Center for Complex Networks & Systems Research, where his research focused on the structure of political communication networks as part of the Truthy project. At LinkedIn, Mike builds machine learning technologies that leverage the behavior and relationships of hundreds of millions of individuals to connect the world’s professionals to make them more productive and successful. Mike’s research on economic and political networks has been featured in Forbes, the Wall Street Journal, Hacker News and on NPR.

Speaker: Minsu Park, Cornell University
Title: Understanding Musical Diversity via Online Media
Date: 08/07/15
Time: 2:00 pm
Room: Informatics East 122
Abstract: Musicologists and sociologists have long been interested in patterns of music consumption and their relation to socioeconomic status. In particular, the Omnivore Thesis examines the relationship between these variables and the diversity of music a person consumes. Using data from social media, Last.fm and Twitter, we design and evaluate a measure that reasonably captures diversity of musical tastes. We use that measure to explore association between musical diversity and variables that capture socioeconomic status, demographics, and personal traits such as openness and degree of interest in music (intones). Our musical diversity measure can provide a useful means for studies of musical preferences and consumption. Also, our study of the Omnivore Thesis provides insights that extend previous survey and interview-based studies.

Speaker: Giovanni Petri, Institute for Scientific Interchange (ISI)
Title: Computational Topology for Complex Networks
Date: 05/15/2014
Time: 1pm
Room: Informatics West 107
Abstract: Topological methods for data analysis have recently attracted large attention due to their capacity to capture mesoscopic features which are lost under standard network technique.
In the first part of this talk I will present an application of a recent method from computational topology, persistent homology, to the characterization of spatial patterns in a mobile phone activity of Milan, with particular reference to national communities within the city’s fabric.
We then show how persistent homology can be extended to the case of weighted networks and present a particular application to fMRI data of patients in different states of consciousness. 

Speaker: Jisun An, PhD candidate, University of Cambridge
Title: Analyzing Social Media for Designing Fit-For-Purpose Systems: From Politics to Business
Date: 11/19/2013
Time: 11am
Room: Informatics East 122
Abstract: Researchers in different disciplines have been studying human behavior in a variety of contexts, and have largely done so upon small-scale data coming from surveys and ethnographic observations. Social media sites now offer a unique opportunity to study individual and social characteristics at scale for a long period of time in unobtrusive ways. In this talk, I will focus on analyses done in two different contexts – political news sharing and micro-investment  – and will show how to translate the corresponding insights into practical implications for the design of fit-for-purpose systems.
Biography: Jisun An is a PhD candidate in a Computer Laboratory at the University of Cambridge and a member of the NetOS group. Her research interest is in analyzing online social media and social network with large-scale data and leveraging its properties to a platform that supports people to make improved choices in social, economic and political domains. Her research lies at the intersection of machine learning, network science, social science, and human computer interaction. For her study, she was funded by EPSRC and she is now an honorable recipient of Google European Scholarship in social computing. Since starting her PhD, she has been fortunate to have opportunities to collaborate with pioneers in social network analysis (e.g., MPI-SWS (Germany), KAIST (South Korea), PARC (USA), and Yahoo! Barcelona (Spain)).

Speaker: Zhong-Yuan Zhang
Title: Semi-Supervised Community Structure Detection in Social Networks Based on Matrix De-noising
Date: 10/15/2012
Time: 1pm
Room: Informatics East 122
Abstract: Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community detection process of the complex social networks remains a challenging problem. We propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of the network, which can also be regarded as the de-noising process of the consensus matrix of the community structures. Our proposed method gives consideration to both the topology and the functions (background information) of the complex network, which improves the interpretability of the results. The comparisons performed on the synthetic benchmarks and the real-world networks show that the framework can significantly improve the detection performance with few constraints, which makes it an attractive methodology in the analysis of complex social networks.

Speaker: Cosma Shalizi, Carnegie Mellon University
Title: Homophily, Contagion, Confounding: Pick Any Three
Date: 11/27/2012
Time: 1pm
Room: Informatics East 130
Abstract: A person’s behavior can often be predicted from that of their neighbors in a social network. This is sometimes explained by homophily, the tendency to form social ties with others because we resemble them.  It is also sometimes explained by social contagion or social influence, the tendency to act like someone because they are our neighbor.  We show that, generically, these two mechanisms are confounded with each other, and with the causal effect of an individual’s attributes on their behavior. Distinguishing them requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular, simple examples show that asymmetries in regression coefficients cannot identify causal effects, and that imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these non-identifiability results.  (Joint work with Andrew Thomas)

Speaker: Robert J. Glushko, School of Information, University of California-Berkeley
Title: The Discipline of Organizing
Date: 04/09/2012
Time: 4pm
Room: Psychology 101
Abstract: We organize things, we organize information, we organize information about things, and we organize information about information. But even though “organizing” is a fundamental and ubiquitous challenge, when we compare these activities their contrasts are more apparent than their commonalities.  As a result different concepts and methods have evolved that are embodied in the disciplines of library science, publishing and content management, business process analysis, data science, information systems design, and other fields that are studied and taught in the ISchools.  These fields don’t always agree in how they approach problems of organizing, the words they use to describe what they do, and in what they seek as solutions.

We propose to unify many perspectives about organizing with the concept of an Organizing System, defined as an intentionally arranged collection of resources and the interactions they support.  Every Organizing System involves a collection of resources, and we can treat things, information, and information about things or information as resources.  Every Organizing System involves a choice of properties or principles used to describe and arrange the resources, and ways of supporting interactions with the resources.  By comparing and contrasting how these activities take place in different contexts and domains, we can identify patterns of organizing and see that Organizing Systems often follow a common life cycle.  We can create a discipline of organizing in a disciplined way.

This new approach cuts across traditional categories of resource collections; we can describe familiar categories like libraries, museums, and business information systems as design patterns that we can then use to apply knowledge about familiar domains to unfamiliar ones; someone with a business or informatics background can better understand libraries and museums and have intelligent conversations with librarians and museum curators… and vice versa.  We now have a generative, forward-looking framework for organizing any collection of resources, especially those that that don’t cleanly fit into the familiar categories, and we can more easily invent new kinds of interactions for them.

This work to develop the Discipline of Organizing is the collective effort of a score of ISchool professors and graduate students from several institutions, and will result in a textbook targeted for the “core” or “gateway” courses at ISchools. This book will be published in early 2013 simultaneously as a printed book, as an ebook, and as an open content repository to support collaborative use and maintenance of the content by all of the ISchools using it.

Bio:

Robert J. Glushko is an Adjunct Full Professor in the School of Information at the University of California, Berkeley.   After receiving his PhD in Cognitive Psychology at UC San Diego in 1979, he spent about ten years working in corporate R&D, about ten years as a Silicon Valley entrepreneur, and now has worked ten years as an academic.  His interests and expertise include information systems and service design, content management, electronic publishing, Internet commerce, and human factors in computing systems.  He founded or co-founded four companies, including Veo Systems in 1997, which pioneered the use of XML for electronic business before its acquisition by Commerce One in 1999.

Speaker: Qiaozhu Mei, School of Information, University of Michigan
Title: The Foreseer: Integrative Retrieval and Mining of information in Online Communities
Date: 03/02/2012
Time: 12:00pm
Room: Informatics West 105
Abstract: With the growth of online communities, the Web has evolved from networks of shared documents to networks of knowledge-sharing groups and individuals. A vast amount of heterogeneous yet interrelated information is being generated for which existing information analysis techniques are inadequate. Current tools often neglect the actual creators and consumers of information, and as a result, the findings are only useful to data analysts. The user-centric Foreseer is the next generation of information analysis for online communities. It represents a new paradigm of study through the four “C’s”: content, context, crowd, and cloud. Information analysis of content is put into the context of the users’ daily lives to benefit the communities (crowd) that generate information residing in the cloud.

In this talk, we will introduce our recent effort of integrative information analysis of online communities. We will highlight the real world problems in online communities to which the Foreseer techniques have been successfully applied. These topics include tracking event evolution and diffusion, detecting misinformation, predicting the adoption of hashtags in microblogging communities, and the prediction of user behaviors in microfinance communities.

Bio: Qiaozhu Mei is an Assistant Professor at the School of Information, the University of Michigan. He received his PhD from the University of Illinois at Urbana-Champaign. He is widely interested in information retrieval, text mining, natural language processing and their applications in web search, social computing, and health informatics. He has served in the program committee of almost all major conferences in these areas. He is also a recipient of the NSF CAREER Award.