On the friendship paradox and inversity: A network property with applications to privacy-sensitive network interventions.

We provide the mathematical and empirical foundations of the friendship paradox in networks, often stated as "Your friends have more friends than you." We prove a set of network properties on friends of friends and characterize the concepts of ego-based and alter-based means. We propose a network property called inversity that quantifies the imbalance in degrees across edges and prove that the sign of inversity determines the ordering between ego-based or alter-based means for any network, with implications for interventions. Network intervention problems like immunization benefit from using highly connected nodes. We characterize two intervention strategies based on the friendship paradox to obtain such nodes, with the alter-based and ego-based strategy. Both strategies provide provably guaranteed improvements for any network structure with variation in node degrees. We demonstrate that the proposed strategies obtain several-fold improvement (100-fold in some networks) in node degree relative to a random benchmark, for both generated and real networks. We evaluate how inversity informs which strategy works better based on network topology and show how network aggregation can alter inversity. We illustrate how the strategies can be used to control contagion of an epidemic spreading across a set of village networks, finding that these strategies require far fewer nodes to be immunized (less than 50%, relative to random). The interventions do not require knowledge of network structure, are privacy-sensitive, are flexible for time-sensitive action, and only require selected nodes to nominate network neighbors.

You are only as safe as your riskiest contact: Effective COVID-19 vaccine distribution using local network information.

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals - those who are most likely to spread the disease. But surveying the population's social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals.

Human social sensing is an untapped resource for computational social science.

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Do Positive Feelings Hurt? Disaggregating Positive and Negative Components of Intergenerational Ambivalence.

Ambivalence has become an important conceptual development in the study of parent-adult child relations, with evidence highlighting that intergenerational relationships are characterized by a mix of positive and negative components. Recent studies have shown that ambivalence has detrimental consequences for both parents' and adult children's psychological well-being. The underlying assumption of this line of research is that psychological distress results from holding simultaneous positive and negative feelings toward a parent or child. The authors question this assumption and explore alternative interpretations by disaggregating the positive and negative dimensions commonly used to create indirect measures of intergenerational ambivalence. Data for the analyses were collected from 254 older mothers and a randomly selected adult child from each of the families. The findings suggest that the negative component is primarily responsible for the association between indirect measures of ambivalence and psychological well-being. Implications of these findings for the study of intergenerational ambivalence are discussed.

When a man hits a woman: moral evaluations and reporting violence to the police.

We use experimental data from a nationally representative sample to examine whether gender and the victim's relationship to the offender affect attitudes about the seriousness of the offense and whether the offense should be reported to the police. We find that respondents are particularly likely to condemn men's assaults on women, and to favor reporting them. The pattern appears to reflect both greater moral condemnation of men's assaults on women and the belief that the victims of these assaults are in greater danger. In general, moral judgments and attitudes toward reporting do not depend on the gender, age, level of education, or political ideology of the respondent. Condemnation of men's violence against women, and support for police intervention when it occurs, are apparently widespread across different segments of the population.