Program and Abstracts
Program
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14:30 Opening
Session 1 Chair: Ralucca Gera (Naval Postgraduate School, USA) [Slides]
14:30-15:00 Keynote Talk
János Kertész (Central European University, Hungary)
First Experiences About the Network Science PhD Program at the Central European University Budapest [Slides]
15:00-15:25 Invited Talk
Sang Hoon Lee (Korea Institute for Advanced Study, Korea)
Network Education in This Country: My Personal Perspective [Slides]
15:25-15:45 Contributed Talk 1
Jermain Kaminski (RWTH Aachen University, Aachen, Germany)
Moviegalaxies: Teaching Social Network Science with Movies
15:45-16:05 Contributed Talk 2
Ralucca Gera (Naval Postgraduate School, USA)
Discovering Complex Networks through Project Based Learning [Slides]
(16:05-16:30 Coffee Break)
Session 2 Chair: Hiroki Sayama (Binghamton University, USA)
16:30-16:50 Contributed Talk 3
Robin Wilkins (University of North Carolina-Greensboro, USA)
Music and Brain Networks: A NetSci Education Project for Training Undergraduate and Pre-College Students in Network Science Methods through Big Data Exploration of the Effects of Music on the Brain [Slides]
16:50-17:10 Contributed Talk 4
Toshihiro Tanizawa (National Institute of Technology, Kochi College, Japan)
A Hands-On Network Science Course for Undergraduate Students in Japan [Slides]
17:10-17:30 Contributed Talk 5
Se-Wook Oh (Mathematical Institute, University of Oxford, UK)*
(*NetSci 2016 free registration award recipient)
Teaching Networks at the University Level: My Experience As a Tutor and TA at Oxford [Slides]
17:30-17:40 Special Guest Talk *New!*
Peter Ruppert (Maven 7, USA/Hungary; Northeastern University, USA)
Introduction of SchoolMapper
17:40-18:00 Contributed Talk 6
Hiroki Sayama (Binghamton University, USA)
Comparison of Curricular Contents and Structures Across Network Science Graduate Courses [Slides]
18:00-18:10 Open Discussions / Closing Remarks
Abstracts
Keynote Talk:
First Experiences About the Network Science PhD Program at the Central European University Budapest
János Kertész (Central European University, Hungary)
After years of planning and a lengthy accreditation procedure at the New York State Education Department we at CEU started our Networks Science PhD Program in the academic year 2015/16, the first in Europe and the second in the world. We have put together a curriculum with compulsory and elective courses and offer the students a multidisciplinary research environment with possibilities in different directions, including economics/finances, data science, sociology, political science, mathematics and environmental science. All our students come from different countries and have different backgrounds. In this talk I will report about our first experiences with this endeavour, about the students’ feedbacks and how we try to improve the program “underway”.
Invited Talk:
Network Education in This Country: My Personal Perspective
Sang Hoon Lee (Korea Institute for Advanced Study, Korea)
Korea is the home of many pioneering and active researchers in the field of network science, as you can tell from the very location of this place hosting the flagship conference of the field, which is held outside Europe and the United States for the first time. In spite of the fact, and the huge popularity of social networking services and big data from general public, the scientific and mathematical aspect of networks is still pretty much unknown to other fields in academia, let alone laypeople. Fortunately, following the network outreach activities to secondary schoolchildren in United Kingdom, I have had chances to give lectures on networks to neuroscientists, college students majoring in physics education, and graduate students in the statistical physics community after I returned to my home country here. As an “ordinary” network researcher without formal pedagogical training (like many of us, I presume), I would like to share some of my ideas on effective ways to teach network science depending on the audience almost purely from trail and error. In particular, I will discuss interesting cultural difference between European and Korean students in terms of classroom attitude, which we have to take into account for teaching (and research presentation as well, in fact). I hope that my explanation, albeit from completely personal experiences, will help you to better understand your colleagues, supervisors, and students from different cultural backgrounds. Finally, partly from my experience on the translation project of the network literacy brochure in which I was involved, I will remark on the language barrier that can discourage “international” students and suggest possible ways to help them to overcome it.
Contributed Talks:
Moviegalaxies: Teaching Social Network Science with Movies
Jermain Kaminski1 Michael Schober2
1 RWTH Aachen University, Technology Entrepreneurship Group, Aachen, Germany
2 Google Inc., Mountain View, USA
Moviegalaxies presents a project that approaches teaching social network science with a medium that is widely known, emotional and thus engaging: Movies. Moviegalaxies, soon to be updated at http://moviegalaxies.com, covers character interaction networks in about 1000 movies ranging from the year 1915 to 2016. Moviegalaxies is the world’s largest database on social networks of characters within screenplays. Since 2011, data and visualizations from Moviegalaxies are used in MOOC such as edX or coursera and at universities such as Columbia, Harvard, Imperial College, MIT, Oxford, Sciences Po and Stanford to teach students about social network analysis. As it turns out, films seem to be a great and engaging vehicle to transfer a scientific theory and its applications. The authors [1] provide a wonderful example on the use of available graph data to teach a group of teenagers at local schools and the Somerville College in Oxford, UK. Besides conveying the basics of nodes, edges and degrees of connections, the platform can illustrate specific network metrics alongside movie examples. For instance, network centrality can be visualized with the main character “Forrest” in the movie Forrest Gump (1994) or “Cooper” in Interstellar (2014), betweenness centrality can be exemplified with the case of Gandalf in The Lord of the Rings: The Return of the King (2003), just as clustering and modularity can be visualized at the example of Babel (2006) or 2001: A Space Odyssey (1968). In the sense that “a picture is worth a thousand words”, interactive visualizations might help to provide a better understanding of networks and their science, even without on-site coaches in a classroom setting. The available material has potential to be further compressed and structured in an interactive online platform “Moviegalaxies:School” with a stepwise information load and complexity, addressing different user levels. As the education path was not planned from the beginning but rather emerged within time, we are still trying to capture the didactic needs of network science education. As such, several questions come to mind that could be addressed in a short talk and discussion:
• Which kind of teaching materials are desired?
• How can the content be made more interactive?
• How can different user (learning) groups be addressed?
• How could a stepwise learning process like at language learning platform Duolingo1 be realized?
• How can quizzes contribute to a better learning outcome?
[1] H. A. Harrington, M. Beguerisse-Díaz, M. ROMBACH, L. M. Keating, and M. A. Porter. Commentary: Teach network science to teenagers. Network Science, 1(02):226–247, 2013.
1 http://duolingo.com
Discovering Complex Networks through Project Based Learning
Ralucca Gera (Naval Postgraduate School, USA)
Exposure and learning Network Science can change one’s view of the world around us as one discovers our connected complex world. This experience can be enhanced through a discovering process rather than being shown the world of networks. This talk presents a method for introducing students to the world of network that can further be adopted in a variety of ways based on the desired learning outcomes of an course. This perspective of learning network science enables the students to feel confident about the topics they just learned, rather defaulting to the instructor as the authority.
Music and Brain Networks: A NetSci Education Project for Training Undergraduate and Pre-College Students in Network Science Methods through Big Data Exploration of the Effects of Music on the Brain
Robin W. Wilkins1,2,3, Michelle Lovett5, David Teachout4 Chelsea Joyce6 and Robert A. Kraft2,3,7
1Network Neuroimaging Lab for Complex Systems, Joint School for Nanoscience and Nanoengineering University of North Carolina-Greensboro, NC USA
2Gateway MRI Center, Joint School for Nanoscience and Nanoengineering University of North Carolina – Greensboro, NC USA
3Office of Research and Economic Development University of North Carolina–Greensboro NC USA
4 Director, Teaching and Learning Center, University of North Carolina Greensboro NC USA
5Southwest High School Guilford County Public Schools Greensboro, North Carolina USA
6Department of Biological Sciences University of North Carolina Greensboro, NC USA
7Department of Biomedical Engineering, Wake Forest University Baptist Medical Center, Winston-Salem NC USA
Integrating research and network science education to equip graduates with the latest ideas, technological know-how and skills is currently a national priority. The pursuit of new ideas, including cross-cutting interdisciplinary research, requires envisioning new formulations of educational approaches and teams that can provide state-of-the-art research opportunities and ensure significant continuing advances across science, engineering, and education; teams that can integrate education and research to support the development of a diverse workforce with cutting-edge capabilities. Now in its third year, this NetSci education broader participation project builds on a successful case study and pilot project that provides hands-on training in network science techniques, including technical skills for working with large neuroimaging data sets, to undergraduate and pre-college students. Harnessing a student’s natural interest in music to foster early training in complex systems and the field of network science, this interdisciplinary project introduces undergraduate and pre-college students to the techniques, tools, and methods from network science and a complex systems approach to working with large data sets. By providing students presently trained and skilled in music with both network science knowledge and hands-on technical training in working with large data, this project aims to explore how a university community-based partnership can leverage a new academic approach by incorporating students from the fine arts into network science. Goals include developing technically trained interdisciplinary researchers with workforce skills prior to the onset of their more formalized academic trajectory and future career or workplace choices. This session reports on the projects’ new formative and summative assessment measurements and graduate traineeship mentoring included with the project. These preliminary outcomes suggest that this program provides a new pathway related to network science learning, technical skill development, and other workplace constructs such as commitment to science and self-efficacy. Broader aims of this project include: to develop young minds that are informed about complex systems and network science techniques; to bridge the gap within the larger interdisciplinary field between the arts, sciences and engineering by converging science, technology, engineering and mathematics; to generate minds that are technically informed and able to pursue a variety of academic and scientific ‘big data’ endeavors; to generate new knowledge about the brain through new network scientific questions; to establish a new pathway for early onset interdisciplinary network science research.
A Hands-On Network Science Course for Undergraduate Students in Japan
Toshihiro Tanizawa (National Institute of Technology, Kochi College, Japan)
In this talk, I sketch the outline of a thirty-hour hands-on course material designed for the fourth-grade students of the National Institute of Technology (Kosen) in the education system of Japan to learn network science for the first time. Most of the students are in the age of nineteen corresponding to the freshman level of undergraduate colleges. The course materials consist of crash courses of network science, python programing, network visualization by Cytoscape or Gephi. After these tutorials, the students are going to pick up and solve a real-world problem from a network perspective through group work.
Teaching Networks at the University Level: My Experience As a Tutor and TA at Oxford
Se-Wook Oh (Mathematical Institute, University of Oxford, UK)
In this talk, I will share my teaching experience as a tutor and TA at Oxford. In the Mathematical Institute at University, lectures are paired with a “class tutorial” to help students better understand the lectures. A class tutorial is organized by a tutor and a teaching assistant (TA). The tutor prepares to teach a class and lead a discussion among students, and the TA marks students’ homework, gives feedback on them, and presents a solution to one of the problems. Based on my experience of being both a TA and a tutor for the networks course at University of Oxford (http://networksoxford.blogspot.co.uk/), which is taught to 4th-year undergraduate students and to Masters students, I will share my thoughts on teaching networks at the university level. Most of the students’ majors were in mathematics, but with no background in networks. Therefore, while the mathematical properties of networks were studied in depth, ideas from different disciplines were also discussed because they often play important roles in the study of networks. Using interactive tools, such as running computer simulations and showing real-world data, helped inspire students’ curiosity about how research in networks is conducted in reality. Accordingly, the class tutorial was organized in an effort to boost students’ learning process --- using the homework assignments, designed by the lecturer, as a guideline. The main activities involves (1) solving mathematical problems in a homework sheet, (2) sharing network-related literature students have read before the class (and occasionally refereeing the papers to encourage students to read critically), and (3) collectively discussing open questions together. Finally, I would like to leave a few notes on tips for teaching networks to students with different backgrounds.
Comparison of Curricular Contents and Structures Across Network Science Graduate Courses
Hiroki Sayama (Binghamton University, USA)
As network science has matured as a well-established field of research, there are now a number of courses on this topic offered at various institutions, primarily at postgraduate levels. In those courses, instructors adopt different approaches with different focus areas and curricular designs. Here we collected information about ~20 existing courses offered on networks from Santa Fe Institute’s Complexity Explorer website and other online/offline sources, and analyzed the contents of their syllabi and/or course schedules. Core concepts and their curricular orderings are visualized as a directed concept network, whose principal structure illustrates the current state of the consensus (or lack thereof) among the network science community about what should be taught about networks at postgraduate levels. The results are also compared to the concept networks generated by K-12 students and educators through the Network Literacy initiative.