Erin Pullen came to the IU Network Science Institute in 2015 from the University of Kentucky, Lexington, where she worked as an assistant professor. She received both her PhD and Master’s degrees in Sociology from the University of Kentucky.

Pullen’s research is primarily egocentric and focuses on medical sociology and health disparities. She works to understand how personal social networks, health behaviors, and health outcomes influence each other, especially among the disadvantaged. Her recent work covers everything from oral health in Mexican-American immigrants (for which she won an award in 2019) to reducing the stigma of mental illness among college students.

Below, learn more about how Pullen hopes to have a positive effect on people with her research and what she plans to work on next. 

Q: Why did you decide to come to IU?

I was definitely lured by the professional opportunities here and the chance to be part of a completely new institute focused on one of my primary areas of research–social networks. Part of the draw of coming to IU was also the brilliant scholars here. I worked under the direction of Brea Perry (IUB Sociology) when I was in graduate school and the experience was transformative for me. Both she and Bernice (Pescosolido, IUB Sociology) are titans of the field, so the opportunity to collaborate with and learn from them was really, really compelling. IU also happens to have a well-respected and highly-ranked Department of Sociology with a strong emphasis on medical sociology, and in particular, the kind of medical sociology that I’m interested in. The research scientist position opened here at a time when I was ready for a change, so there was a timing element involved too.

Q: Where does your research focus?

I would say that broadly my work focuses on understanding the reciprocal relationship between chronic health problems and networks. From a methodological standpoint, my research tends to use survey and egocentric network data, and I am very invested in research design and ethics–ensuring that social science and network research are done in a scientifically rigorous way to answer important questions, primarily focused on the health of disadvantaged or stigmatized populations.

After finishing my PhD, I was very drawn to mental health and substance use research, but since I’ve been here, I’ve had the opportunity to collaborate on a variety of projects emphasizing different aspects of health and well-being. So my research has expanded since I’ve come here, which I enjoy. I’m very intellectually curious, so learning about new areas of research and broadening my thinking on health, well-being, and social relationships has been very welcome. 

Q: Why do you think this research is important?

For me, it’s important that I feel like what I’m doing helps people, either by contributing to our understanding of health problems that hopefully yields insights that can help people lead healthier lives or by focusing on marginalized populations. I like to think that the work I do helps people and that’s part of the reason I chose to focus on health. Health is relevant to every single person and–good or bad–shapes our life trajectories in really important ways.

I was lucky enough to have two fantastic mentors in graduate school who study health, Brea and Carrie Oser, so I received encouragement from them and had access to health data. I also happen to come from a family of health care providers, including my mother and several aunts, and I recognized early on the value of good health. Understanding how people manage their health, how they navigate the challenges of living with a chronic illness, the ways stigma and prejudice shape well-being–these questions are pertinent now and will continue to be long into the future.

Q: What are some projects you’re working on now?

Right now I’m working on a project with Brenda Heaton from Boston University, and we’re looking at women living in housing developments and the ways networks shape their oral and general health. I’m also working on a project that includes principal investigator Karla Wagner at the University of Nevada and Hank Green (IUB Public Health) that looks at networks, beliefs, and behavior related to HIV among a racially diverse sample of women.

I have a project with IU sociology graduate students Emily Ekl and Christopher Turner looking at general social service data and how having networks with people who have mental illness can influence your views of mental illness. I’m also analyzing data from the College Toolbox Project.

I recently applied for a grant with an anthropologist based in New York, Melissa Basil, where we propose examining the influence of support and other networks on treatment adherence and mental health outcomes among people with cystic fibrosis.

Q: That is quite the variety of projects. Do you enjoy the spread?

Absolutely. Part of what I love about this job is that I get to work on such a diversity of things. I’m able to do this, in part, because I’m not splitting my time between teaching and a large amount of service obligations that faculty housed in departments manage. Though I appreciate the value of routine, I also like having day-to-day variability and being exposed to new ideas and ways of thinking. It’s challenging, but intellectually stimulating. 

Read more of Erin’s recent work:

1. Elaine Hernandez, Erin Pullen, and Jonathan Brauer. 2019. “Social Networks and the Emergency of Health Inequalities Following a Medical Advance: Examining Prenatal H1N1 Vaccination Decisions.” Social Networks 58: 156-167. DOI: 10.1016/j.socnet.2019.03.002.
2. Carrie Oser, Kathi Harp, Erin Pullen, Amanda Bunting, Danelle Stevens-Watkins, & Michele Staton. 2019. “African American Women’s Tobacco and Marijuana Use: The Effects of Social Context, Substance Use Beliefs, and Risk Perceptions.”  Substance Use & Misuse 54(6): 873-884. DOI: 10.1080/10826084.2018.1528464.
3. Erin Pullen, Brea Perry, and Gerardo Maupomé. 2019. “Talking About Teeth: The Effects of Egocentric Network Characteristics on Oral Health Outcomes in a Mexican-American Immigrant Community.” Advances in Medical Sociology 19: 205-122. DOI: 10.1108/S1057-629020190000019006.

Xiaoran Yan, an assistant research scientist at the IU Network Science Institute, joined the team after serving as a postdoctoral research associate in the Information Sciences Institute at the University of Southern California. 

Yan’s theoretical interests remain on mathematical theories and models of networks, focusing on community structures and dynamical processes on networks. Yan received his PhD in Computer Science from the University of New Mexico and spent time as a graduate fellow at the Santa Fe Institute. 

Read on to find out how Yan homed in on data and why he values the interdisciplinary nature of IUNI.

Q: What does your research focus on? 

Previously, I was a theoretically-focused computer scientist. So I’d design these elaborate algorithms and make proofs of how fast they are and how accurate they are and would pretend to run actual experiments with them. I’ve since found the real value lies in the data itself. Without data, you can make your theory-crafting as sophisticated as you want, but it won’t lead to real application that can be applied to the real world.

My research combines computer science, which focuses a lot on algorithm building, and basically finding machine-learning or AI-related techniques to apply to network data. But when you have to apply these algorithms to social systems or biological systems, for example, you have to at least be informed about what they’re trying to do. So that’s where the sense of collaboration started and I rendered myself to this interdisciplinary adventure.

Q: Were collaboration and interdisciplinary research factors in you joining IUNI?

I’m a computer scientist by training, but in my dissertation years, my advisor was trained as a physicist. I had statisticians and biologists on my dissertation committee, so I was exposed to this interdisciplinary way of thinking and doing research in the early stages of my career, and that’s the nature of network science. So the whole idea of an institute across disciplines and boundaries with so many different departments and schools joining forces — I haven’t seen anything like that before.

My other big draw for IUNI was they had these giant bibliographic datasets like the Web of Science. And at the end of my PhD, I was realizing more and more that I need to work with real data and the Web of Science is one of the best datasets you can get your hands on and get familiar with just by being part of the academia.

Q: What led you to get into informetrics?

I started being a bibliographic researcher after I came to IU. Informetrics is interesting to me because, as researchers, we are all part of it. Unlike biological networks or financial networks, which I’ve never been part of, I like working with scientometric data because the scholarly networks that help people publish are already embedded in our lives, so we feel directly associated with them.

Q: How has bibliographics spread into your projects?

I work a lot on CADRE (Collaborative Archive & Data Research Environment) and the more I deal with data, the more I realize how important data is — and just generally the lack of standards and lack of agreement in the bibliometrics field. To be truly reproducible and scientifically unbiased, you have to take into account how the data is collected, cleaned, modeled, all the way through to the end — and that’s the big picture for CADRE. With another project, Science Genome (YY Ahn, IUB Informatics), we are focusing on a graph-embedding algorithm. Embedding allows us to transform all the different information from bibliographic datasets into a common mathematical representation.

Q: What are some other projects you’re working on?

I work on a project with Liana (Apostolova, IUSM) where we are creating an algorithm that’s trying to figure out how a combination of factors contributes to the cognitive decline in Alzheimer’s disease. I’m also involved in a climate change project that Santo (Fortunato, IUB Informatics) has put together where we study climate change from a data science point-of-view. Another project I’ve been involved with for a long time is about brain networks, and I collaborated with Olaf (Sporns, IUB Psychological and Brain Sciences) on that. 

Read some of Xiaoran’s recent publications:

1. Efficient network navigation with partial information by X. Yan, O. Sporns, A. Avena-Koenigsberger
2. The impact of air transport availability on research collaboration: A case study of four universities by A. Ploszaj, X. Yan, K. Börner
3. Weight thresholding on complex networks by X. Yan, L. G. S. Jeub, A. Flammini, F. Radicchi, S. Fortunato
4. Weighted Stochastic Block Models of the Human Connectome across the Life Span by J. Faskowitz, X. Yan, X. Zuo, O. Sporns
5. Graph Filters and the Z-Laplacian by X. Yan, B. M. Sadler, R. J. Drost, P. L. Yu, K. Lerman

Alice Patania joined the IU Network Science Institute as a research scientist in 2017. She received her PhD in Applied Mathematics at Politecnico di Torino while working at the ISI Foundation in Turin, Italy. 

In this Q&A, Patania shares why she decided to take her theoretical mathematics background into the world of complex systems, data science, and higher-order networks and talks about some of the projects she’s working on with Olaf Sporns (IUB Psychological and Brain Sciences) and Liana Apostolova (IUMS Neurology).

Q: What brought you to IUNI?

I did my PhD at ISI in Torino and studied complex systems, and there I started to work on higher-order networks. So in networks, you look at relationships between two actors but in higher-order networks, you are taking into consideration more complex interactions, which happen between three or more actors together. It’s not a concept that is new because, in one way or another, people have been studying them for 50 years, but the research has been very descriptive or case specific and it didn’t generalize well to other areas or applications. 

So the idea is to use what has been developed in mathematics over the past 100 years and make it more accessible to researchers in sociology, biology, computer science, and neuroscience—the way it has been done with network science and graph theory—and that’s why I came here. Being at a small institution that specializes in network science would give me the freedom to do higher-order networks because it’s something that’s very new and out there.

Q: Why are you interested in higher-order networks?

There has been very little easy translatable math of higher-order networks—there are a lot of questions that were not easily solvable using math because mathematicians are more interested in finding out how things work and if things work, instead of building a way to make things work, because there might be only one way in a trillion to build it. So I got very intrigued by trying to translate the theoretical insights I had into something that could be useful. 

Q: What else are you interested in?

During my theoretical studies, I realized early on I really liked the problem-solving part of mathematics more than writing proofs. I was happy to find out that data science gives me the opportunity to use mathematics to create new ways to study complex problems. Similar to data science, you need a lot of creativity to work in math because you need to think outside the box with something that is not tangible at all. So you’re trying to learn what the function is beyond your data. For me, it’s the most fun.

Q: So what are you working on right now?

A lot of the projects I have are database and data related, so they try to take into consideration an entire network and see the network as a higher-order element of the dataset and then look at the entire collection of networks and study its properties.

One project I’m working on with Olaf (Sporns) is about how brain networks evolve as people get older, or how a healthy person’s brain changes throughout their lifetime. It’s hard to understand the entire development of the changes in connections in a healthy person, while in Alzheimers’ disease you know you’re losing connections. Another project I have is with Liana (Apostolova), where we are trying to find genetic biomarkers in blood for mild cognitive impaired adults associated with Alzheimer’s disease. 

Q: What sort of project would you like to work on next?

Since I moved here, I’ve been more and more interested, especially with the work that’s been done at IUNI on the massive Web of Science dataset and other huge datasets, in trying to find ways of building higher-order models that are defined locally, so you can build a higher-order model from what you can see locally in your dataset. It would be a way to describe the entire dataset or network in a way that’s more accessible. You can act on the network locally despite there being no way to look at the entire thing because it’s so huge—so my focus has slightly shifted to these local models for global understanding.

Read more of Alice’s work:

1. Topological gene-expression networks recapitulate brain anatomy and function by Patania, A., Selvaggi, P., Veronese, M., DiPasquale, O., Expert, P., & Petri, G.
2. The shape of collaborations by Patania, A., Petri, G., & Vaccarino, F.
3. Construction of and efficient sampling from the simplicial configuration model by Young, J. G., Petri, G., Vaccarino, F., & Patania, A.

Be sure to follow Alice on Twitter.

The IU Network Science Institute recently welcomed its inaugural class of IUNI Fellows, a group of postdoctoral scholars who are working in cross-cutting and cutting-edge areas of networks research. 

At IUNI, we talk openly and often about the research each of our team members is working on in hopes of sparking new collaborations and sharing expertise, which is a critical practice in fostering interdisciplinary approaches to networks research at Indiana University. The IUNI Fellows program is an effort to support cross-cutting collaborations in the field. Fellows spend time in their home labs and with other Fellows and IUNI research scientists. 

Meet our Fellows. 

Evgeny Jenya Chumin

PhD Medical Neuroscience, Indiana University School of Medicine

Evgeny Jenya Chumin, who goes by Jenya, is currently working in Sporns Lab (IUB) on brain and structural networks in Alzheimer’s Disease and the clinical application of network neuroscience to the disease, such as through neuroimaging of brain networks. One of his main aims is to make this research more digestible for clinical physicians and researchers. The research could aid in earlier identification of those who are at-risk for Alzheimer’s, even before symptoms appear.

Adam Roth

PhD Sociology, Washington State University

Adam Roth is a research fellow in the Department of Sociology (IUB) and is working with the Leveraging Neuroimaging Biomarkers to Understand the Role of Social Networks in Alzheimer’s Disease (SNAD) project with Drs. Brea Perry, Liana Apostolova, and Andrew Saykin. In his work for SNAD, Roth studies how egocentric networks affect the progression of Alzheimer’s Disease. Roth also studies how the changing social environments or isolation of aging adults affects their health.

Sofia Teixeira

PhD Information Systems and Computer Engineering, Universidad de Lisboa

Sofia Teixeira works in the Center for Social and BioMedical Complexity (IUB). Teixeira develops computational models and simulation frameworks to better understand the dynamics of complex networks in changing biological and social systems. One project she is currently working on studies the spread of HIV in the sex workers network in Brazil and the factors that affect the spread or prevention of the disease. 

Meichen Yu

PhD Computational Neuroscience, VU University Medical Center Amsterdam

Meichen Yu is a postdoctoral appointee at the Center for Neuroimaging and the Indiana Alzheimer Disease Center at the Indiana University School of Medicine. Yu is working to identify the genetic architecture of brain networks in Alzheimer’s Disease by developing new network and connectivity measures to better understand the brain. He is also performing EEG and MEG network analysis of Alzheimer’s and frontotemporal dementia.

 To learn more about our IUNI Fellows, visit our website.

On Oct. 4, Brian Nosek came to Indiana University to discuss the big challenges around improving transparency and reproducibility in research, as well as steps the scientific community can take to shift research culture in that direction. 

Nosek, a psychology professor at the University of Virginia and the co-founder and executive director of the Center for Open Science, said his core research interest centers on the gap between values and practices in research.

His work is dedicated to closing that gap by changing the cultural incentives and norms around research to encourage reproducibility and rigor in science. For Nosek, this open, transparent culture will be critical in maximizing the efficiency and pace of science.

Nosek began by introducing sociologist Robert Merton’s norms of scientific ethos which are:

• Communality: ability to review a researcher’s path to end results vs. secrecy in methodology 
• Universalism: evaluating research on its own merit vs. evaluating based on researcher’s reputation
• Disinterestedness: motivated by knowledge and discovery vs. science as a competition
• Organized skepticism: considers all new evidence, open to scrutiny vs. considering only evidence that benefits researcher
• Plus one Nosek added: quality vs. quantity of research 

In a 2007 study Nosek cited, the vast majority of surveyed researchers endorsed these norms, even as a smaller majority admitted they were practitioners of them. However, the majority of the researchers believed other researchers don’t embrace those norms.

Building a framework

This ingrained belief within the scientific community that people do not participate in the scientific code of conduct is why we need to focus on a broader culture change that incentivizes researchers to align themselves with these ethos, Nosek says.

Open, transparent policies can help–but they must also become deep-rooted within the culture to be embraced.

At the foundation of the journey to a policy change is the infrastructure that makes the practice of creating open, transparent research possible. Working upward, the next steps include creating a user interface to make implementing the practice easy, changing communities to make it normative, creating incentives to make it rewarding, and then, finally, establishing the policy that makes it required. 

Nosek and his team are encouraging behavior changes by advocating for journals to stamp articles with “behavior badges” for Open Data, Open Materials, and Preregistration of analysis plans. He’s also changing incentives for research by creating open calls for results that are typically not rewarded, such as negative and null results and replications of previous studies. 

To view the Center for Open Science’s top guidelines for better aligning current practices with the ideal scientific norms, visit the Center for Open Science site. 

Other resources Nosek shared include:

• OSF: https://osf.io
• TOP Guidelines: https://cos.io/top/
• Preregistration: https://cos.io/prereg/
• Registered reports: https://cos.io/rr/
• Badges for open practice: https://cos.io/our-services/open-science-badges/
• OSF Registration: https://cos.io/our-products/osf-registries/
• Brian’s slides: https://osf.io/95ge7/

View the full talk here: https://youtu.be/RLLoxxtBu50

Today the IU Network Science Institute and the Network Science Institute at Northeastern University are pleased to announce AccelNet-MultiNet, a new collaboration to strengthen and expand the international community of network science researchers working in the area of multilayer networks.

The project, funded by the National Science Foundation, heavily integrates four European partner organizations: Universitat de Barcelona (Spain), ISI Foundation (Italy), Central European University (Hungary, Austria), and Centre de Physique Théorique (France).

IUNI Director Santo Fortunato is the IU principal investigator, along with co-principal investigator Ann McCranie, IUNI’s assistant director of research administration. Leading from Northeastern are Alessandro Vespignani and Kate Coronges.

A centerpiece of this five-year project is building international exchanges between scholars in the U.S. and Europe who are working in the area of multilayer network science. We are accomplishing this by funding 40 graduate and post-doctoral fellow exchanges between labs and institutes to work on cutting edge science that spans the two groups. Essentially, we are asking these young scholars to become brokers between two communities. Built into the exchange and fellowship program is training in international collaborations, network science, and “collabathon”activities that result in publication and presentations.

AccelNet-MultiNet will also create a new international webinar series in Fall 2020 that will feature leading scholars talking about their newest research. This monthly series will be intertwined with our shared collaborations but will also be publicly available and archived. We hope this series is appealing to the many researchers interested in these problems and creates a form of virtual water-cooler, where new and exciting work doesn’t have to wait for the next annual conference to get a wide airing.

Finally, NSF-funding will allow us to organize three international summits on multilayer network science that we hope will set the agenda and create solutions for some of the pressing methodologically- and application-based inquiries in this field. These meetings will take place in the U.S. and Europe.

If you are interested in following our work, you are welcome to follow us on Twitter and sign up for periodic newsletter updates on announcements related to the project.

Read more about this project: IU release and Northeastern release.

View the AccelNet-MultiNet website here: https://www.accelnet-multinet.org/

AccelNet-MultiNet is funded by NSF grants 1927418 and 1927425. 

IUNI is thrilled to announce that we will be hosting this historic joint conference, merging the annual meetings Sunbelt and NetSci!

Networks 2021: A Joint Sunbelt and NetSci Conference

July 6-11, 2021 in Washington, DC (Hyatt Regency on Capitol Hill)

http://networks2021.net  

The leadership of the International Network for Social Network Analysis (INSNA) and the Network Science Society (NSS) are excited to announce their first joint annual conference in 2021. This meeting will provide an opportunity for interdisciplinary students, scholars, and practitioners to come together and share their ideas and work on the science of networks. It will feature innovative plenary talks, parallel and poster presentation opportunities. In addition, there will be preconference workshops and satellite meetings, and a School in the tradition of NetSci that offers lectures from distinguished networks researchers. 

Networks 2021 is hosted by the Indiana University Network Science Institute with help from the Network Science Institute at Northeastern University and Science of Networks in Communities (SONIC) Research Group at Northwestern University. Networks 2021 is led by Steering Committee Members: 

Representing the International Network for Social Network Analysis

• Ulrik Brandes, ETH Zürich 
• Kayla de la Haye, University of Southern California 
• David Lazer, Northeastern University

Representing the Network Science Society

• Vittoria Colizza, Inserm 
• Raissa D’Souza, University of California, Davis
• Yamir Moreno, University of Zaragoza, ISI Foundation

Hosting Committee

• Noshir Contractor, Science of Networks in Communities (SONIC) Research Group at Northwestern University (previously on INSNA and NSS boards)
• Kate Coronges, Network Science Institute at Northeastern University
• Santo Fortunato, Indiana University Network Science Institute
• Ann McCranie, Indiana University Network Science Institute (On NSS board)
• Bernice Pescosolido, Indiana University Network Science Institute (On INSNA board)
• Alessandro Vespignani, Network Science Institute at Northeastern University (previously on NSS board)

For more information and to sign up for periodic announcements about opportunities to participate, please visit our website at http://networks2021.net and follow us on social media (@networks2021 on Facebook, Instagram, and Twitter). 

Science Genome, a project that will translate the fundamental questions of how scientific fields and discoveries evolve using new statistical and analytical methods, has received its Minerva Research Initiative funding. 

YY Ahn (IUB Informatics), the project’s lead, says the project is ramping up with new students, computing resources, and help from IUNI, whose team will build the software infrastructure for the project.

Science Genome will collaborate with another major IUNI-partner project, the Collaborative Archive & Data Research Environment (CADRE). 

CADRE is a platform that allows researchers to examine millions of scientific publications in massive bibliometric datasets, such as the Web of Science and Microsoft Academic Graph, and perform analyses and create visualizations of those datasets.

Science Genome’s initial award period is three years, with funding awarded for an additional two years upon the U.S. Department of Defense’s approval of the project’s progress, totaling up to $4.4 million.

Ahn’s team includes Staša Milojević, Alessandro Flammini, and Fil Menczer (IUB Informatics).  Sriraam Natarajan, who is now at the University of Texas at Dallas, is also a collaborator on the project. IUNI’s Xiaoran Yan and Valentin Penthev will play a key role in Science Genome’s development. 

Project potential

Ahn says Science Genome will allow researchers to formulate new quantitative questions about how fields evolve into vector-space algebra and dynamics by using graph embedding methods. 

“In science of science, you deal with many different entities—authors, papers, and journals. Although they are closely linked together, it’s not easy to analyze them in a unified framework,” Ahn said. “Once you represent these entities as vectors living in the same space, it will become much easier to formulate fundamental questions about the dynamics of scientific enterprise with a completely new perspective.”

Ahn refers to Science Genome as a “useful, compact representation” of each entity, or an author, paper, or journal, that can succinctly capture the essential characteristics of each. With these representations, concepts such as similarity between an author and a paper can be developed with a simple mathematical formula. If the team wants to see the evolution of scientific enterprises, for example, they could simply observe how the vectors change over time and ask whether there is a general law behind it. 

Science Genome can then be leveraged to create metrics and methods to assess the potential of scientific discoveries and innovations for specific scientific enterprises—from individual teams to entire countries.

Read more about the project here.