Filipi N. Silva is the latest addition to the IUNI team, joining us last month as an assistant research scientist.
Silva first came to Indiana University Bloomington back in 2017, when he visited IU as a visiting scholar for a week. At the time, Silva was a postdoctoral fellow at the University of São Paulo, where he earned his Ph.D. in physics under the supervision of Luciano da F. Costa.
Later in 2017, he returned to IU to work as a visiting scholar with IU SICE for a year.
Now Silva has made his third trip back to IU, but this time he’s here to stay as an IUNI research scientist. Silva will contribute his network visualization expertise and knowledge of network science and science of science to IUNI’s research, as he works alongside IUNI Director and SICE Professor Santo Fortunato, as well as other IU faculty.
In this Q&A, Silva talks to us about what he’s working on at IUNI and why he loves the work he does.
Q: What brought you to Bloomington, Ind., from Brazil?
In Brazil we have a program in which the universities support students that want to collaborate with international institutions as a visiting scholar. I was applying with Diego R. Amancio (associate professor of computer science at the University of São Paulo), and we came here to Indiana University and had meetings with Fil (Filippo Menczer; IUB Informatics) and Santo. Their science of science research matched what we were doing, and it was really interesting and they were really receptive. Then a few months later, we decided to use our funding to come here. So I was here for a year as a visiting scholar.
Q: What did you work on as a visiting scholar at IU?
One thing I worked on with Fil at SICE was Scholarometer, which is a crowdsourced tool that provides new information on researchers using Google Scholar. The tool already existed but it broke, so we redesigned it and added other things. Another thing we worked on, where Santo and Alessandro (Alessandro Flammini; IUB Informatics) joined us, was understanding the dynamics of science. We were trying to visualize the evolution of science: You have fields that merge, you have fields that splits into others, you have fields that pop up from nothing, things like that. But we didn’t finish it before I left.
Q: So you’re working on that project now that you’re back? What else are you working on?
Yes, I’m still working on the project. It’s called “Identifying and predicting the birth of new disciplines.” But I’m also working on another project with Santo where we analyze how authors are cited. We have a new model in which we try to predict how authors overall will be cited over the following years, given the information from previous years. I also started another one with Santo in which we are trying to find a better way to define how strong a community is based on network perturbations.
I’m also working on some projects with my colleagues from the University of São Paulo that include the analysis of political data and understanding the effects of spatiality on diversity for certain network dynamics, such as opinion-forming models.
Q: A major area of your focus is science of science research—why SoS?
I really like science of science because it’s like using science to understand itself. Science of science can also help us understand where we are going, it can give us a view of the future, and an overview of the present. It can also be used to understand the past: How did things come together to make new technology or breakthroughs? So it’s very exciting work.
Q: Your work centers around network visualization. What led you to focus on this?
Well, let’s start with networks. I think networks are interesting because you can model many kinds of systems—I’ve worked with biologists, mathematicians, physicists, sociologists. I think network science is a very powerful way to model very real-world problems.
The second part is visualization in general. Visualization has a way for us humans to see what’s happening. We are very good at detecting patterns visually, so if you can detect patterns, even if there is complex data, you can better understand what’s happening.
If we merge networks and visualizations, you have a powerful tool to visualize anything. That’s the thing. Because networks can be applied to model many real systems and at the same time, visualization is a way to get that information more easily. It’s very powerful—you can see several aspects of real-world systems just by looking at the network visualizations. That’s what captivated me to work on this kind of thing.
Read some more of Silva’s recent work:
- Representation of texts as complex networks: a mesoscopic approach by F. de Arruda, F. N. Silva, V. Q. Marinho, D. R. Amancio, L. da F. Costa
- Patterns of authors contribution in scientific manuscripts by A. Corrêa Jr, F. N. Silva, L. da F. Costa, D. R. Amancio
- Using network science and text analytics to produce surveys in a scientific topic by F. N. Silva, D. R. Amancio, M. Bardosova, L. da F. Costa, O. N. Oliveira Jr.
- Visualizing Complex Networks (CDT-5) by F. N. Silva, L. da F. Costa
- Topological characterization of world cities by S. Domingues, F. N. Silva, C. H. Comin, L. da F Costa