11. Executive Meeting:

• Organization of the WTFNS 2018 conference

22. CommonTies: a context-aware nudge towards social interaction (Azza Abouzied) - 9:00AM-10:15AM, A5-1139

• Urbanization has created transient, ethnically-varied, and densely-populated communities where meaningful human contact is difficult. Urban social norms such as "civil inattention" - a deliberate display of unwillingness to become more familiar with strangers - discourage social interactions among strangers. While these norms help reduce anxiety or fear in overcrowded urban centers they hinder meaningful social interactions in public spaces (coffee shops, museums, and malls, etc.) and events (conferences, galas, etc.) where such interactions should occur. This paper describes CommonTies, a simple technological nudge that managers of interaction spaces and organizers of social events can use to leverage contextual information to encourage social interactions among strangers.

Together with Jay Chen.

25. The neural signatures of affective social relations and the emergence of dyadic reciprocity and inequality in human groups (Peter Bearman - Columbia University) - 3:30PM-5:00PM, A5-101

• Humans are a fundamentally social species, and the social networks in which we are embedded significantly determine our physical and psychological well being, shape what is possible for us to achieve and imagine, and provide the context for social action. Given their importance and their complexity, it makes sense to think that the effectively navigating the interactions within these networks requires efficient mechanisms for processing complex multivalent social information about network members. This ability is so important that it may be among the foremost computational challenges that influenced our evolution, particularly the dramatic development of our "social brains." This talk considers a set of findings from socializing cognitive social neuroscience that captures neural and social network data at multiple time points for interacting groups. We believe that we can identify neural mechanisms for the reproduction of inequality in popularity in small groups. We likewise discover a truly interpersonal mechanism for the emergence of reciprocity, the building block of social solidarity. We show that we can predict from neural signatures who group members will like five months in the future.

This talk is co-sponsored by the SRPP Seminar Series.

11. On the global job search (Kinga Makovi) - 9:00AM-10:15AM, A5-1139

25. Research design (Manu Munoz) - 9:00AM-10:15AM, A5-1139

8-10. WTFNS

• Workshop on the Frontiers of Network Science is an opportunity for our group to bond over research - some that is more early stage, and some that is better developed. It also allows us to bring leading scholars from the field of Network Science who bring their work to NYUAD. The keynote speakers' talks are open to the NYUAD community. Take a look at who they are, and what they will present on!

22. Optimal diversification strategies in the networks of related products and of related research areas​ (Aamena Alshamsi - Masdar Institute/Khalifa University) - 9:00AM-10:15AM, A5-1139

• Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks.

26. Information transmission in a social network: a controlled field experiment (Paolo Pin - Bocconi) - 13:15PM-2:30PM, A5-101

• Using an app for smartphones we run an experiment among high school students to study the pattern of aggregation of sparsely distributed information when competing agents are arranged in small networks and can share only non-verifable pieces of information. Our understanding is that the level of cooperation is high, especially among students that belong to the same class. Nevertheless the level of centralization of the network significantly affects the final results, with the most central node benefiting in terms of payoffs. By adding a second node with a high centrality we see that the results change significantly, with more signals traveling through the links. We then turn to a parsimonious level-k approach to characterize players according to their behavior in the game. When estimating the model we see that data are consistent with a vast majority of players acting cooperatively, with the difference across networks driven mainly by a small share of strategic players.

This talk is organized by Rebecca Morton.

6. Hiding individuals and communities in a social network (Talal Rahwan - Masdar Institute/Khalifa University) - 9:00AM-10:15AM, A5-1139

• The Internet and social media have fueled enormous interest in social network analysis. New tools continue to be developed and used to analyze our personal connections, with a particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools? By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence, and security agencies may better understand how terrorists escape detection. We first study how an individual can evade ``node centrality'' analysis while minimizing the negative impact that this may have on his or her influence. We prove that an optimal solution to this problem is hard to compute. Despite this hardness, we demonstrate how even a simple heuristic, whereby attention is restricted to the individual's immediate neighborhood, can be surprisingly effective in practice, e.g., it could easily disguise Mohamed Atta's leading position within the WTC terrorist network. We also study how a community can increase the likelihood of being overlooked by community-detection algorithms. We propose a measure of concealment, expressing how well a community is hidden, and use it to demonstrate the effectiveness of a simple heuristic, whereby members of the community either ``unfriend'' certain other members, or "befriend" some non-members, in a coordinated effort to camouflage their community.

13. Ethnic diversity increases scientific impact (Bedoor AlShebli - Masdar Institute/Khalifa University) - 9:00AM-10:15AM, A5-1139

• Inspired by the numerous social and economic benefits of diversity, we analyze over 9 million papers and 6 million scientists spanning 24 fields of study, to understand the relationship between research impact and five types of diversity, reflecting (i) ethnicity, (ii) discipline, (iii) gender, (iv) affiliation and (v) academic age. For each type, we study group diversity (i.e., the heterogeneity of a paper's set of authors) and individual diversity (i.e., the heterogeneity of a scientist's entire set of collaborators). Remarkably, of all the types considered, we find that ethnic diversity is the strongest predictor of a field's scientific impact (r is 0.77 and 0.55 for group and individual ethnic diversity, respectively). Moreover, to isolate the effect of ethnic diversity from other confounding factors, we analyze a baseline model in which author ethnicities are randomized while preserving all other characteristics. We find that the relation between ethnic diversity and impact is stronger in the real data compared to the randomized baseline model, regardless of publication year, number of authors per paper, and number of collaborators per scientist. Finally, we use coarsened exact matching to infer causality, whereby the scientific impact of diverse papers and scientists are compared against closely matched control groups. In keeping with the other results, we find that ethnic diversity consistently leads to higher scientific impact.