SONIC Speaker Series Presents: Hank Green

SONIC  is proud to welcome Dr. Hank Green of Indiana University. He will speak on Wednesday, Nov 6th  at 10:30 am in Frances Searle Building, Room 3-417. Please contact Carmen Chan with any questions.

Hank Green
Associate Professor

Department of Applied Health Science, Indiana University School of Public Health

Indiana University Network Science Institute


Network-based Strategies for Improving PrEP Availability Among those at High Risk forHIV

Pharmaceutical Pre-Exposure Prophylaxis (PrEP) has been proven effective against HIV infection, but it is underutilized as an HIV prevention strategy in part due to a lack of familiarity with PrEP treatment regimes among healthcare providers that serve the most at risk communities.  Lack of healthcare provider knowledge has been identified as a substantial contributor to the continuing increase of HIV infections among sexual and gender minority patients. An Institute of Medicine report states, “few physicians are knowledgeable about or sensitive to lesbian, gay, bisexual, and transgender (LGBT) health risks or needs.” Nevertheless, physicians often serve as key points of access for this preventive treatment among minority patients who may not know the full range of prevention options. Thus, physicians’ lack of knowledge is likely to limit the highest risk patients’ awareness of, screening for, and access to PrEP. In this study, we propose to develop network-based strategies that help identify physicians who serve populations at high risk for HIV but who prescribe PrEP infrequently and those physicians likely to influence the prescribing behavior of their peers, facilitating the dissemination of information about PrEP via strategically targeted information campaigns.

Physician networks inferred from medical claims data protect patient identities but enable insights into the numbers and kinds of patients shared by physicians across the country and approximate the structure of physicians’ professional communication networks. Prior research has shown that physician networks inferred from shared patients are strongly predictive of the diffusion of shared medical practices. Medical knowledge is exchanged among physicians who share patients with one another because they often go to each other for advice about new treatments, difficult cases, etc. Medical claims data can provide more reliable information than data provided by physician surveys because the latter typically exhibit low response rates and a high likelihood of nonresponse bias. Additionally, acquiring a complete national set of medical claims is affordable whereas acquiring a set of complete national physician surveys would be prohibitively expensive.

Using national-scale claims data and the network generated from them, we tested hypotheses that ask whether PrEP prescribing behavior in physician sub-communities varies based on the whether a physician 1) is or has close ties to an infectious disease specialist, 2) shares patients with PrEP prescribing physicians, 3) shares patients with a doctor that treats a high proportion of HIV patients, 4) treats patients covered by specific insurance providers. While testing these and other hypotheses, we will account for contextual and geographic factors including location of providers in states with high incidence of HIV or in regions with high stigma against MSM. Understanding which of these network features may be associated with knowledge of and prescription of PreP will lead to new strategies that identify doctors who may be most influenced to begin or increase their recommendation of PrEP or who are positioned to shape the PrEP prescribing behavior of their peers, ultimately increasing its utilization among those at risk for HIV. The strategy, while focused here on PrEP, is broadly generalizable for any innovation in medical care that would benefit from a more targeted approach. This study has 3 aims:

Aim 1. Construct a physician-to-physician network relevant to the population indicated for PrEP. We used aggregated medical claims from both private and public payers to create a physician-to-physician network using standard network approaches. The data included all claims for patients being treated with drugs uniquely prescribed for STIs such as HIV and genital herpes, establishing a baseline of shared patients representative of the population indicated for PrEP.

Aim 2. Test alternative hypotheses for processes that may drive PrEP prescription rates. We used information about the distribution of PrEP prescribing in this network to assess the relative contribution of 1) social influence 2) differential awareness of PrEP, 3) geographic variation related to features of the HIV epidemic (such as stigma), and 4) other network based features such as clustering to prescription rates while controlling for factors such as the rural or urban location of a doctor.

Aim 3. We will use the structure of the physician network developed in Aim 1 and the results of our analyses in Aim 2 to identify targets for a strategic information campaign.  We will generate a series of rules that identify physician characteristics associated with being an appropriate target and an appropriate peer change agent. Then, based on those rules we will simulate the diffusion of PrEP information and PrEP prescribing behavior in the network we generated in Aim 1. We will also contact a small set of targets and change agents to assess the accuracy of our rules and determine the opinions of these physicans regarding the broader utility of such targeted campaigns for increasing PrEP prescribing, fundamentally affecting the PrEP care cascade.

To learn more about the speaker, please visit:

SONIC Speaker Series Presents: Céline Rozenblat

The SONIC Speaker Series presents

Céline Rozenblat

University of Lausanne (UNIL), Switzerland

A Multi-Level Network Perspective on Complex Urban Systems

SONIC Lab is proud to welcome Dr. Céline Rozenblat of the University of Lausanne. She will speak on Tuesday, May 21st at 10 am in Frances Searle Building, Room 1-483. Please contact Brent Hoagland with any questions.

Abstract: In the context of knowledge and information societies, new tendencies in the long/medium term evolution of urban systems, together with new data and methods, require that existing theoretical assumptions and conceptualizations be challenged as global urban hierarchies are reconfigured. The connection between urban systems at different levels of organization becomes more and more relevant for understanding urban systems and their transformations. But the current inter-urban perspective is not sufficient to encompass these dynamics. Other cognitive, social, and institutional proximities in the innovation processes combine with spatial proximities. It leads us to consider cities in several dimensions of proximities in a multilayer perspective. The evolution of power distributions inside and between cities reshapes the world organization of central/peripheral cities and the complexity of the global urban system. Actors as multinational firms, or high-level innovation centers, participate actively in these reconfigurations that concentrate wealth, control, innovation, and attractiveness in a few cities. In the complexity of this multi-level system, how is regionalization of the world reshaping in a multipolar urban world? How does the multi-level perspective highlight some resilience properties? The methodologies derived from complex systems sciences bring new forms of intelligibility to worldwide urban dynamics.

Céline Rozenblat, is professor of Urban Geography at the University of Lausanne, Switzerland and president of the urban commission of the International Geographical Union (IGU). She studies systems of cities at European and world scales, multinational firm networks, inter-urban dynamics, comparative urban data, mapping and visualization of networks in geography, and spatial analysis. For several years she has worked on the relations between the evolution of multi-level urban processes and dynamics in city-system networks. To study these topics comparatively, she has built many databases on large European and worldwide cities and the networks they form. In particular, she has dealt since 1990 with databases on multinational firm networks and on city properties and evolution in a multi-dimensional and long temporal approach. Diachronic and dynamic studies supply materials to develop spatial and dynamic models and visualizations. She participated in European projects like ESPON FOCI 2008-2011, FP7 FET Insite (2011-2013) and Multiplex (2012-2016). She also participates to the EuropeAid project with China MEDIUM (2015-2019) on medium size cities in China and launched two projects LOGIICCS (FNS 2015-2018) and MEDIUM (SWISS-Conf. 2016-2018) on modeling Indian and Chinese cities’ integration in global networks of multinational firms and innovation.

SONIC Speaker Series presents: Jake Fisher

The SONIC Speaker Series presents

Jake Fisher

Survey Research Center at the University of Michigan’s Institute for Social Research

Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties

SONIC Lab is proud to welcome Dr. Jake Fisher of the University of Michigan. He will speak on Monday, April 15th, 2019 at 10 am in Frances Searle Building, SONIC Conference Room 1-459. Please contact Brent Hoagland with any questions.


Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model—representing the distances in social space—or a series of networks drawn from those probabilities—representing routine churn in the network—are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, the author explores the properties of the model. The author shows that the model produces smoothed diffusion results, which predict attitudes in future waves 10 percent better than a diffusion model using the observed network and up to 5 percent better than diffusion models using alternative, non-model-based smoothing approaches.

The forthcoming paper is available online at:

Jacob C. Fisher is a research investigator in the Survey Research Center at the University of Michigan’s Institute for Social Research. His research focuses on developing and testing better network diffusion models, to understand how ideas, innovations, and information spreading through a group of people help to create and maintain a common culture over time.  He obtained his doctorate in sociology and his master’s in statistical science from Duke University.   His work has appeared in Sociological Methodology, Network Science, Social Currents, and other venues.

SONIC Speaker Series presents: Anirban Mukherjee

The SONIC Speaker Series presents

Anirban Mukherjee

Marketing Faculty at INSEAD

Investigating the Multiple-Source Effect in Product-Pitch Videos

SONIC Lab is proud to welcome Prof. Mukherjee of INSEAD. He will speak on Tuesday, April 2nd, 2019 at 11 aM in Frances Searle Building, Room 1-483. Please contact Brent Hoagland with any questions.


Conventional wisdom suggests that having multiple speakers (“sources”) deliver content enhances persuasion. Laboratory evidence confirms the folk knowledge and terms it the “multiple-source effect.” As the prior evidence was developed in the behavioral laboratory, it derives from relatively simple laboratory stimuli conveying information on far fewer topics than is typical in the real-world marketplace. Our study addresses this limitation. We investigate the multiple-source effect in all (more than 30,000) product-pitch videos in nine product-innovation-related categories on Kickstarter, an online crowdfunding portal, since its inception in April 2009 to mid-February 2017. We use deep-learning models to algorithmically measure the number of speakers, transcribe and analyze the spoken content, and measure other audial and visual control variables. We document a novel boundary condition of the multiple-source effect—the effect depends on the number of topics discussed in the video. In simpler videos discussing fewer topics, which is more similar to stimuli in prior laboratory studies, we corroborate prior findings that having more speakers leads to more funding. However, in more complex videos discussing more topics, we find that having more speakers does not affect funding. The latter is consistent with the literature on information overload. More broadly, our research demonstrates the potential of deep learning to enable the analysis of large-scale audio and video data in order to investigate human behavior in real-world settings.

Prof. Mukherjee is Visiting Assistant Professor of Marketing at INSEAD and Fellow of the Institute on Asian Consumer Insight at Nanyang Technological University. Prior to INSEAD, Prof. Mukherjee was Assistant Professor of Marketing at the Lee Kong Chian School of Business at Singapore Management University. He holds a B.Sc. in Electrical and Computer Engineering (2003), and a M.Sc. and Ph.D. in Marketing (2008, 2009), from Cornell University. He studied at The Doon School, Dehra Dun (353 KA, 1999).

Prof. Mukherjee is an expert in quantitative and computational marketing methodology. He develops and applies cutting-edge methods to managerially and substantively important marketing phenomena. His work has been published in prestigious journals (such as the Journal of Marketing Research, Journal of Retailing, and Management Science), featured in popular press outlets (including Forbes), and received several awards (including several best paper awards). He has been invited to give research talks at numerous prestigious universities and he consults and teaches for several major companies (such as IBM, LinkedIn, and Sony).

SONIC Speaker Series presents: George G. Vega Yon

The SONIC Speaker Series presents

George G. Vega Yon

Department of Preventive Medicine at USC’s Keck School of Medicine

Big Problems for Small Networks: Statistical Analysis of Small Networks and Team Performance

SONIC Lab is proud to welcome George G. Vega Yon of USC’s Keck School of Medicine. George will speak on Wednesday, March 20th, 2019 at 2PM in Frances Searle Building, Room 1-483 with a workshop to follow. Please contact Brent Hoagland with any questions.



Small network data such as team, family, or personal networks, is common in many fields that study social networks. Although the analysis of small networks may appear simplistic relative to the difficulties posed by “big” datasets, there are at least two key challenges: (1) fitting statistical models to explain the network structure in small groups, and (2) testing if structural properties of small networks are associated with group-level outcomes; for example, team performance. In this presentation, we introduce two new statistical methods that use a revisited version of Exponential Random Graph Models (ERGMs) in the context of small networks. Using exhaustive enumeration of networks in the support, we are able to calculate exact likelihood functions for ERGMs, which allows us to obtain maximum likelihood estimates directly (without using simulations), avoiding common problems that arise from methods that rely on approximations instead. This is joint work with Prof. Kayla de la Haye (USC).

A workshop on the R packages ergmito and gnet for applying the methods introduced during the talk will be conducted.

George G. Vega Yon is a Biostatistics Ph.D student and Research Programmer in the Department of Preventive Medicine at USC’s Keck School of Medicine. His interests are in computational statistics and scientific software development. Most recently, his research has focused on the development of statistical methods for both phylogenetics and social network analysis. He holds a MS degree in Economics from Caltech, and a MA in Economics and Public Policy from Universidad Adolfo Ibáñez, Chile.

SONIC Speaker Series presents: Prasad Balkundi

The SONIC Speaker Series presents

Prasad Balkundi

Organization and Human Resources, University of Buffalo

The Negative Side of the Social Ledger: A Meta-Analysis

SONIC Lab is proud to welcome Prasad Balkundi of the University of Buffalo. Dr. Balkundi will speak on Wednesday, April 18th, 2017 at 10 AM in Frances Searle Building, Room 1-483. Please contact Dr. Michael Schultz with any questions.



Despite a resurgence of research on negative ties in social networks, a comprehensive understanding of negative and positive has yet to be provided. Incorporating evidence from prior 163 independent samples we examine whether the initiation of positive and negative relationships (i.e., out-degree) or the reception of positive and negative relationships (i.e., in-degree) is more impactful to the focal employee’s effectiveness. Furthermore, to address the negative asymmetry hypothesis in social networks, we compare the relative importance of positive versus negative work relationships while holding the directionality constant. This meta-analytic review makes five contributions to theory on negative and positive social networks by (a) demonstrating the undermining impact of negative ties on performance and job attitudes; (b) providing information on the negative asymmetry hypothesis within social networks to reveal that negative ties occur less frequently than positive ties and that any asymmetry effects depend on the relative number of negative ties to positive ties in the context; (d) distinguishing between haters (senders of negative ties) and jerks (receivers of negative ties) to illustrate that haters have worse job attitudes than jerks, but the two do not differ on performance; and (e) providing positive and negative affect as antecedents to negative ties. Implications of these findings along with study limitations and future research directions are discussed.​

Prasad Balkundi is Chair and Associate Professor in the Organization and Human Resources Department at the University of Buffalo School of Management. Prof. Balkundi’s teaching and research interests are in social networks and team processes. He has published in the Academy of Management Journal and Leadership Quarterly. He is active in the Academy of Management National Conference and has presented several papers over the years.

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SONIC Speaker Series presents: Gianluca Carnabuci

The SONIC Speaker Series presents

Gianluca Carnabuci

Organizational Behavior, ESMT Berlin

Good For One But Bad For Most? How Intra-Organizational Networks Impact Innovative Performance At The Inventor And Firm Level

SONIC Lab is proud to welcome Gianluca Carnabuci of ESMT Berlin. Dr. Carnabuci will speak on Wednesday, April 26th, 2017 at 10 AM in Frances Searle Building, Room 1-483. Please contact Dr. Michael Schultz with any questions.



Extant organizational research suggests that R&D scientists tend to be more productive (i.e., they generate more impactful innovations) when they occupy a central position within their organization’s intra-organizational collaboration network. Does this imply that an organization’s overall innovative performance would increase if the organization encouraged its R&D scientists to pursue more central network positions? We address this question using a multi-level panel dataset describing the evolving intra-organizational networks of 140 semiconductor firms, as well as the individual network position of each of their R&D scientists. We proceed in three steps. First, we confirm that network centrality does enhance scientists’ innovative performance within our empirical sample, even after controlling for unobserved individual- and organizational-level differences. Second, we simulate how the overall intra-organizational network of an organization would change if it enacted two distinct norms of collaboration, each encouraging scientists to increase their network centrality. We find that norms of “diffuse” collaboration increase network cohesion, whereas “star-centric” ones increase network centralization. Third, we study how these network-level properties affect innovative performance among our semiconductor firms. We find that network cohesion enhances organizational-level innovative performance only under conditions of high knowledge diversity, while network centralization always reduces it. These findings show that scientists’ pursuit of network centrality may have opposite performance effects at the individual- and organizational-level. A counterintuitive normative implication is that, under quite broad conditions, organizations would enhance their innovative performance by discouraging (rather than encouraging) their R&D personnel from increasing their centrality within the intra-organizational network.


Gianluca Carnabuci is an associate professor (with tenure) of organizational behavior who joined ESMT Berlin in broad conditions, organizations would enhance their innovative performance b 2016. Previously he was an associate professor of organization and management at the University of Lugano and an assistant professor at Bocconi University. He holds a PhD in Social and Behavioral Sciences from the University of Amsterdam.

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SONIC Speaker Series Presents: Sadat Shami

The SONIC Speaker Series presents

N. Sadat Shami

Director, Center for Engagement and Social Analytics IBM

The Application of Social Analytics to Understand and Improve Organizational Outcomes

SONIC Lab is proud to welcome N. Sadat Shami of IBM. Dr. Shami will speak on Monday, February 26th, 2017 at 10 AM in Frances Searle Building, Room 1-483. Please contact Dr. Michael Schultz with any questions.



The increased adoption of social media in the enterprise provides an opportunity for organizations to receive real-time feedback from employees on organizational issues. Enterprise social media provides a platform for employees to express their thoughts and opinions on organizational programs, policies, and strategies through unstructured text in status updates, blogs, online community forums etc. Such textual data can be mined to generate insights about the employee experience. Research has shown that organizations that take into consideration employee feedback in organizational decision-making are more productive, and have employees that are more engaged with the organization. In this talk, he will first describe the design and use of Social Pulse – a tool to make sense of large- scale social media text generated by employees of an organization while preserving privacy. He will then describe how social media text mined by Social Pulse, combined with an organization’s hierarchical network structure can be used in statistical models to predict outcomes of interest to an organization, focusing on the particular case of employee engagement, and how it spreads in an organization.

N. Sadat Shami leads Talent Development, Engagement and Social Analytics for IBM. He has responsibility for analytics, insights and strategy around leadership, learning, inclusion, employee engagement and IBMer social media. His team of researchers and practitioners focus on enabling better business decisions by applying AI techniques on large-scale social media, social network, and enterprise data. He has led several advanced analytics projects in the employee engagement and social space, showing linkage with various outcomes of interest to IBM. Sadat has a PhD in Information Science from Cornell University, has published over 20 articles in highly selective peer-reviewed conferences and journals, and has lived in five countries.

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SONIC Speaker Series presents: Johan Ugander

The SONIC Speaker Series presents

Johan Ugander

Management Science & Engineering Stanford University

Ruffled Feathers: Trait inference beyond homophily

SONIC Lab is proud to welcome Johan Ugander of Stanford University. Dr. Ugander will speak on Tuesday, February 6th, 2017 at 10 AM in Frances Searle Building, Room 1-483. Please contact Dr. Michael Schultz with any questions.



The observation that individuals tend to be friends with people who are similar to themselves, commonly known as homophily, is a prominent and well-studied feature of social networks. While homophily describes a bias in attribute preferences for similar others, it gives limited attention to variability. In this work, we observe that attribute preferences can exhibit variation beyond what can be explained by models of homophily. We call this excess variation monophily to describe the presence of individuals with extreme preferences for a particular attribute possibly unrelated to their own attribute. We observe that monophily can induce a similarity among friends-of-friends on a network without requiring any similarity among friends. In order to independently simulate homophily and monophily in synthetic networks, we contribute a new model of social network structure that we call the overdispersed stochastic block model (oSBM), an extension of the classical stochastic block model. We use this model to demonstrate how homophily-based methods for predicting attributes on social networks based on friends, “the company you keep,” are fundamentally different from monophily-based methods based on friends- of-friends, “the company you’re kept in.” To illustrate the differences between homophily and monophily-based prediction we place particular focus on predicting gender, where homophily can be weak or nonexistent in practice. These findings offer an alternative perspective on network structure and attributes in general and prediction in particular, complicating the already difficult task of protecting privacy on social networks. This is joint work with Kristen Altenburger.

Johan Ugander is an Assistant Professor at Stanford University in the Department of Management Science & Engineering, within the School of Engineering. His research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale social data.

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