Network Science for Social Good 2019

Networks are steadily being employed to understand diverse problems such as epidemic forecast, the spreading of information or disinformation, the impact of sudden onset emergencies, and to estimate population demographics. Recently, research efforts addressing the needs of the most vulnerable populations have also started to draw the attention of the broader scientific community. Notable examples are the Data for Development and the Data for Refugee challenges organized by Orange and Türk Telekom, respectively, and academics from our field, as well as the various data for social good initiatives and conferences such as Bloomberg’s Data for Social Good Exchange. However, the gap between the academic world and the organizations that could use the proposed methods and insights for their programmatic purposes is still wide.

This workshop intends to foster the discussion of network science’s role in addressing societal challenges. Showcasing relevant theoretical, empirical and operational work within network science research that can be applied for the benefit of humanity. We encourage participation of researchers working in all areas that concern the interaction of network science, complexity sciences, machine learning, and society.

We are looking forward to seeing the best of your new insights in the field of network science for social good.

Call for Abstracts

Venue

The meeting is a satellite of the NetSci2019 conference and will take place at the University of Vermont Davis Center, Burlington, Vermont , USA on Monday, May 27th, 2019.


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This satellite workshop focuses on interdisciplinary research from all aspects of network science applied to:

  • Identifying and measuring vulnerabilities: Examples of vulnerabilities include poverty, lack of information, exposure to conflict, resilience to natural disasters, epidemic risk, bullying, and discrimination.


  • Network interventions: Some of the related topics to Network Interventions are: establishing causal relationships in social networks; network statistical inference; network intervention applications and theory; data-driven intervention design; applications to public health; crime prevention; bullying in elementary school; detecting, understanding, and combating fake news; maximize the spread and diffusion of information and behaviors; mitigating the polarization of opinions; maximize creativity and innovation processes in organizations.


  • Algorithmic equity: Algorithms play an increasingly important role in network science, from recommendations to information dissemination. Focusing on research that addressed new types of vulnerabilities, such as detecting algorithmic biases, or inferring biases in data where the most vulnerable are underrepresented.

Submission Instructions

Submissions should be at most two pages long and include the following information: title of the talk, author(s), affiliation(s), e-mail address(es), abstract and one or two figures.

Abstract submission deadline is March 10, 2019. Acceptance notifications will be sent by April 2.

Submit your abstract via the EasyChair: Submission Link

Invited Speakers

TBA

REgistration

Satellite participants (with or without abstract submission) will have to register following the procedure described in the NetSci2019 conference web site: Registration Link

Participants who only wish to participate in the satellite and do not wish to attend the main conference will have the option to register only for a single day.

For planning purposes, if you are considering attending the meeting, even as an auditor, please register to this Eventbrite link

Organizers

  • Vedran Sekara, UNICEF Innovation
  • Cristian Candia Vallejos, Universidad del Desarrollo & MIT Media Lab
  • Elisa Omodei, UNICEF Innovation
  • Andrés Abeliuk, University of Southern California
  • Manuel Garcia-Herranz, UNICEF Innovation
  • Flavio Pinheiro, Universidade Nova de Lisboa
  • Diana Orghian, MIT Media Lab


Contact the organizers by sending an email to: vsekara@unicef.org or ccandiav@mit.edu