NetSci 2017 will feature a number of plenary session speakers whose work is outstanding in the field of networks.
Meeyoung Cha (KAIST), Alex Fornito (Monash), Lise Getoor (UC Santa Cruz), César A. Hidalgo (MIT), Shawndra Hill (Microsoft Research NYC & U Penn), Maximilian Schich (UT Dallas Arts & Technology), M. Ángeles Serrano (U Barcelona), Roberta Sinatra (Central European Univ), and Xiaofan Wang (Shanghai Jiao Tong Univ)
Danielle S. Bassett
University of Pennsylvania
Danielle S. Bassett is the Eduardo D. Glandt Faculty Fellow and Associate Professor in the Department of Bioengineering at the University of Pennsylvania. She is most well-known for her work blending neural and systems engineering to identify fundamental mechanisms of cognition and disease in human brain networks. She received a B.S. in physics from the Pennsylvania State University and a Ph.D. in physics from the University of Cambridge, UK. Following a postdoctoral position at UC Santa Barbara, she was a Junior Research Fellow at the Sage Center for the Study of the Mind. In 2012, she was named American Psychological Association's `Rising Star' and given an Alumni Achievement Award from the Schreyer Honors College at Pennsylvania State University for extraordinary achievement under the age of 35. In 2014, she was named an Alfred P Sloan Research Fellow and received the MacArthur Fellow Genius Grant.
In 2015, she received the IEEE EMBS Early Academic Achievement Award, and was named an ONR Young Investigator. In 2016, she received an NSF CAREER award and was named one of Popular Science’s Brilliant 10. She is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Her work has been supported by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Army Research Laboratory, the Alfred P Sloan Foundation, the John D and Catherine T MacArthur Foundation, and the Office of Naval Research.
LINKS Center for Social Network Analysis, University of Kentucky
Our field has been blessed with a plethora of centrality concepts: literally dozens of named measures and hundreds of identified variants. Curiously, though, we have difficulty giving a thoughtful to answer the question “what is centrality?” or “how do tell a measure of centrality from a measure of anything else?”. Inherent in this situation is our inability to say what centrality measure should do, and what constitutes a well-formed member of the class. This leaves empirical researchers with little guidance for what measure to choose for a given problem, other than Brass (1984) used such and such a measure in a well-cited piece. This paper explores three broad approaches to defining centrality measures. Ultimately, no definitive answers are provided, but the exploration is, in this author’s opinion, enlightening. Moreover, it provides a way to both collapse existing measures into fewer categories, while at the same time pointing the way to generating bespoke measures uniquely appropriate for specific research questions.
Steve Borgatti is a Professor and Paul Chellgren Endowed Chair at the University of Kentucky in the Management Dept. of the Gatton College of Business and Economics. His research is focused on social networks, particularly in the context of organizations. His primary research interest is social network analysis with additional interest in cultural domains and knowledge management. His dissertation was on regular equivalence.
He serves as an Associate Editor for the Journal of Supply Chain Management and also Computational and Mathematical Organizational Theory. He was a founding editor of Field Methods, and still sits on their editorial board. He was a Senior Editor at Organization Science, and sat on the editorial boards of Administrative Science Quarterly, Connections, Organization Science, Journal of Management and Sociological Methodology.
He was recently elected President of INSNA, the professional association for social network researchers. In the 1990's, while serving two terms as President of INSNA, INSNA was incorporated and the Sunbelt conference was brought under INSNA's umbrella. During that time, he also founded the SOCNET listserv. Previously he ran the NSF Summer Institute for Ethnographic Research Methods in Anthropology (founded by Russ Bernard and Bert Pelto).
Jennifer A. Dunne
Sante Fe Institute
Network science has provided powerful tools for analyzing and modeling many aspects of the organization, function, and stability of ecosystems. In particular, network-based approaches to the study of complex species interactions have led to new understanding of general patterns and processes of ecological structure and dynamics, and of the community-level consequences of species loss and other perturbations. However, most such research has focused on extant systems, on systems that are putatively human-free, and on trophic networks (e.g., food webs). This talk will discuss recent reconstructions and analyses of deep-time ecological network data, from hundreds to hundreds of millions of years ago, that move beyond these constraints. Research on ancient systems is providing new ways to address questions about the sustainability of modern socio-ecological systems.
Jennifer’s research interests are in analysis, modeling and theory related to the organization, dynamics and function of ecosystems, with a focus on ecological networks. Using cross-system analysis and computational modeling, Jennifer seeks to identify fundamental patterns and principles of ecological network structure and dynamics at multiple spatial and temporal scales. Such research provides a useful framework for understanding ecological robustness and persistence, including how humans fit into and impact ancient, historic, and current ecosystems.
Her research has been covered in Scientific American, Wired, SmartPlanet, ScienceNow, and Nature News. She has served as an editor at the Journal of Complex Networks, Ecology Letters, and Oikos, is an Oxford Series in Ecology and Evolution editor, and is an advisor to the science and culture magazine Nautilus.
Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.
Meeyoung Cha is an associate professor at Graduate School of Culture Technology in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook's Data Science Team as a Visiting Professor for a year.
Monash Institute of Cognitive and Clinical Neurosciences
Functional segregation and integration are two fundamental pillars of brain organization. Functional segregation is supported by a modular topology and functional integration is supported by high inter-connectivity of hub regions (rich-club organization). In this talk I will present evidence from macroscale brain imaging in humans that this interplay between segregated and integrated activity is dynamic and context-dependent. Combining mesoscale tract-tracing, transcriptomics and functional magnetic resonance imaging in the mouse, we have found that the activity of hub regions is dominated by low-frequency dynamics and that hub connectivity has a distinctive genomic signature that is characterized by elevated coexpression of genes regulating energy metabolism. We have also found that this genomic signature of hub connectivity is conserved across species and resolution scales, being apparent in the microscale nervous system of the nematode worm C elegans. Our findings are consistent with a hierarchy of dynamical timescales and metabolic demand in the brain, such that hub regions integrate information over wide temporal windows and at high energetic cost.
Alex Fornito completed a PhD in the Departments of Psychology and Psychiatry at the University of Melbourne, Australia, followed by Post-Doctoral training at the University of Cambridge, UK. He is currently an Associate Professor, Australian Research Council Future Fellow, and co-Director of the Brain and Mental Health Laboratory in the Monash Institute of Cognitive and Clinical Neurosciences. Alex’s research uses cognitive neuroscience, network science and graph theory to understand brain network organization in health and disease. In particular, he focuses on the development and application of new methods to understand how brain networks dynamically adapt to changing task demands, how they are disrupted by disease, and how they are shaped by genetic influences. Together with co-authors Andrew Zalesky and Ed Bullmore, he recently published the first text book on network analysis for neuroscience, entitled Fundamentals of Brain Network Analysis.
University of California Santa Cruz
Network data (e.g., communication data, financial transaction networks, data describing biological systems, collaboration networks, the Web, etc.) is ubiquitous. While this observational data is useful, it is usually noisy, often only partially observed, and only hints at the actual underlying social, scientific or technological structures that give rise to the interactions. For example, an email communication network provides useful insight, but is not the same as the “real” social network among individuals. In this talk, I introduce the problem of graph identification, i.e., the discovery of the true graph structure underlying an observed network. This involves inferring the nodes, edges, and node labels of a hidden graph based on evidence provided by the observed graph. I show how this can be cast as a collective probabilistic inference task and describe a scalable approach to solving this problem.
Lise Getoor is a Professor in the Computer Science Department at the University of California, Santa Cruz. Her research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data. She has over 200 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, serves on the DARPA ISAT Study Group (2016-2019) and the board of the Computing Research Association (CRA), and was co-chair for ICML 2011. She is a recipient of an NSF Career Award and eleven best paper and best student paper awards. In 2014, she was recognized by KDD Nuggets as one of the emerging research leaders in data mining and data science based on citation and impact. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a Professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013.
César A. Hidalgo
Massachusetts Institute of Technology
How do networks learn? How do teams learn how to produce new products? Develop new skills? Start new industries and develop new research areas? Where do they get the knowledge they need? How is that learning affected by changes in technologies, institutions, and culture? In this presentation I will present work exploring how networks learn and how learning is affected by geography, industrial relatedness, and technology. Also, I will present tools that we have created to facilitate collective learning in teams and nations.
César A. Hidalgo leads the Macro Connections group at The MIT Media Lab and is also an Associate Professor of Media Arts and Sciences at MIT. Hidalgo's work focuses on collective learning. That is, the learning that takes place in teams, organizations, cities, and nations. In his lab he develops analytical tools to improve our understanding of how collective learning takes place, and also, he develops data visualization and analysis tools designed to improve the collective learning of organizations. Hidalgo's academic publications have been cited more than 7,500 times and his visualization engines have been viewed more than 100 million times. Hidalgo is the author of Why Information Grows (Basic Books, 2015), the co-author of The Atlas of Economic Complexity (MIT Press, 2014), and a co-founder of Datawheel LLC.
Microsoft Research NYC & University of Pennsylvania
A wide variety of data is available on consumers, including data that they themselves have made available online that is accessible by the public for free. Online users talk a lot about TV shows and brands online, and the resulting data can be used to measure TV and brand audiences and help marketers answer questions about who is talking, what they are saying, where to find them and who is connected. However, the demographics and networks of online users are typically hidden from public view and need to be inferred. We propose the use of ``Talkographics", which consists of gathering publicly available text data produced by users to learn their group-level demographics, interests and networks. We combine data from Twitter and online surveys about brands and TV shows in a novel way that enables group-level prediction of demographics and interests of a large number of audiences. In addition, we demonstrate that group-level predictions can be used reliably in the context of building affinity networks and recommendation systems and for individual-level prediction.
Shawndra Hill is a Senior Researcher at Microsoft Research NYC . Before joining Microsoft, she was an Assistant Professor in the Operations and Information Management at the Wharton School of the University of Pennsylvania, where she is still an Annenberg Public Policy Center Distinguished Research Fellow, a Wharton Customer Analytics Initiative Senior Fellow, and a core member of the Penn Social Media and Health Innovation Lab. Generally, she researches the value to companies of mining data on consumers, including how consumers interact with each other on social media -- for targeted marketing, advertising, health and fraud detection purposes. Her current research focuses on the interactions between TV content and Social Media (www.thesocialtvlab.com). Dr. Hill holds a B.S. in Mathematics from Spelman College, a B.E.E. from the Georgia Institute of Technology and a Ph.D. in Information Systems from NYU's Stern School of Business.
UT Dallas Arts & Technology
Why should network scientists be interested in art and culture? Why should historians of art and culture be interested in network science? Why does NetSci2017 officially call for contributions in "arts and design"? And why does the main conference feature a session on "culture"? This talk will provide reasoning regarding these questions, both documenting the rise of a vibrant community, and outlining challenges that are central to both network science and the study of art and culture. A NetSci satellite theme with more than 60 contributions from more than 37 disciplines since 2009, network analysis now permeates data-driven research in art and culture, while culture analytics increasingly establishes itself as a science.
Springer Complexity Invited Talk
Maximilian Schich is an associate professor for arts and technology at the University of Texas at Dallas and a founding member of the Edith O’Donnell Institute of Art History. His work converges hermeneutics, information visualization, computer science, and physics to understand art, history, and culture. His motivation is to harness and advance expertise in collaboration, to build and lead a group of researchers, to teach students, and to contribute within a team of teams. Maximilian is the first author of A Network Framework of Cultural History (Science Magazine, 2014) and a lead co-author of the animation Charting Culture (Nature video, 2014). He is an editorial advisor at Leonardo Journal, an editorial board member at Palgrave Communications (NPG), and the Journal for Digital Art History. He publishes in multiple disciplines and speaks to translate his ideas to diverse audiences across academia and industry. His work received global press coverage in 28 languages.
M. Ángeles Serrano
University of Barcelona
Complex networks display a hidden metric structure, which determines the likelihood and intensity of interactions. This quality has been exploited to map real networks, producing geometric representations that can be used as a guide for their efficient navigation and that shed light on pivotal forces --like preferentiality, localization, and hierarchization-- that rule their structure and evolution. Now, the powerful methods that unveil network geometry enable to disentangle the multiple scales coexisting in real networks, strongly intertwined due to the small world property. We have defined a geometric renormalization group for complex networks embedded in an underlying space that allows for a rigorous investigation of networks as viewed at different length scales. We find that real scale-free networks show geometric scaling under this renormalization group transformation. This feature enables us to unfold them in a self-similar multilayer shell which reveals the coexisting scales and their interplay. The multiscale unfolding brings about immediate practical applications. Among many possibilities, it yields a natural way of building high-fidelity smaller-scale replicas of large real networks, and sustains the design of a new multiscale navigation protocol in hyperbolic space which boosts the success of single-layer versions.
M. Ángeles Serrano obtained her Ph.D. in Physics at the Universitat de Barcelona (UB) in 1999 with a thesis about gravitational wave detection. In 2000, she also received her Masters in Mathematics for Finance at the CRM-Universitat Autònoma de Barcelona. After four years in the private sector as IT consultant and mutual funds manager, Prof. Serrano returned to academia in 2004 to work in the field of Network Science. Subsequently, she was a researcher at Indiana University (USA), the École Polytechnique Fédérale de Lausanne (Switzerland), IFISC Institute (Spain), and held a Ramón y Cajal research associate appointment at UB until October 2015. The results of her investigations are summarized in major peer reviewed international scientific journals - including Nature, PNAS, PRL, book chapters, and conference proceedings. Prof. Serrano leads and participates in several research projects at the international and national levels. She is also actively involved in advising and research supervision. She serves in evaluation panels and program scientific committees, and acts as a reviewer in several international journals. In February 2009, she obtained the Outstanding Referee award of the American Physical Society. She is a Founder Member of Complexitat, the Catalan Network for the study of Complex Systems, and Promoter Member of UBICS, the Universitat de Barcelona Institute of Complex Systems.
Central European University
In most areas of human performance the path to major accomplishments requires a steep learning curve, long practice and many trials. Athletes go through years of training and compete repeatedly before setting new records; musicians practice from an early age and perform in secondary venues before earning the spotlight. Yet, little is known about the quantitative patterns that lead to success in creative fields. In this talk we provide a quantitative framework to describe the evolution of success in scientific and artistic careers, and ask: Are there quantifiable signs of an impending career hit? Is the success of a particular work predictable? Are there network measures that improve our understanding of success? We show that in scientific careers impact, as measured by influential publications, is distributed randomly within a scientist’s sequence of publications, and that this random impact rule allows us to formulate a stochastic model to uncouple the effects of productivity, individual ability and luck, unveiling the existence of universal patterns governing the emergence of scientific success. Further we focus on trajectories of visual artists, and show that the prestige of institutions, quantified through network measures, fully determines an artist’s future success. Starting in prestigious venues increases the chance of exhibiting in more venues, appealing to a more international audience, and of being successful in the auction market.
Roberta Sinatra is Assistant Professor at the Center for Network Science and at the Math Department, Central European University (Hungary), and a Visiting Research Faculty at the Network Science Institute, Northeastern University (USA). She is a theoretical physicist by training, working at the forefront of network and data science, developing novel theoretical methods and analyzing empirical data sets on social phenomena and human behavior. Currently, she spends particular attention on the analysis and the modeling of information and dynamics that lead to the collective phenomenon of success. Roberta completed her studies in Physics at the University of Catania, Italy, and spent time as a visiting research student in Universities and Research centers in Zaragoza (Spain), London (UK), and Vienna (Austria). In 2012 she joined the Barabasilab in Boston, first as James McDonnell Postdoctoral fellow, then as Research Assistant Professor, leading the group working on Science of Success.
Department of Automation, Shanghai Jiao Tong University, China
There has been a lot of researches on coordination behaviors on complex networks, however, competitive behaviors are also very common on real world complex networks. This talk will introduce our recent works on analysis and control of competitive behaviors on complex networks. In particular, the talk will focus on the influence of network structure and positions of competitors on the result of competition. We first consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another one in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with several centrality-based criteria. Then, we investigate the influence maximization problem in which a competitor tries to add a number of links so as to maximize the relative influence of the competitor over the other one. Finally, we generalize the model to the case with more than two competitors.
Xiaofan Wang received the Ph.D. degree from Southeast University, China in 1996. He has been a Professor in the Department of Automation, Shanghai Jiao Tong University (SJTU) since 2002, a Distinguished Professor of SJTU since 2008, and the deputy dean of Zhiyuan College of SJTU since 2010. He received the 2002 National Science Foundation for Distinguished Young Scholars of P. R. China, the 2005 Guillemin-Cauer Best Transactions Paper Award from the IEEE Circuits and Systems Society, the 2008 Distinguished Professor of the Chaing Jiang Scholars Program, and the 2015 Second Class Prize of the State Natural Science Award. His current research interests include analysis and control of complex dynamical networks. He is currently the Chair of the IFAC Technical Committee on Large-Scale Complex Systems and the Chair of the Chinese Technical Committee on Complex Networks and System Control.