Structure and Function of Antagonistic Ties

The Structure and Function of Antagonistic Ties in Village Social Networks

Image courtesy of Cavan Huang.

Our social experience is influenced not only by our positive but also by our negative connections. Using uncommon data from 176 isolated villages in Honduras, we investigate how social network structure and function might be affected by negative ties amid the positive ties of friendship and kinship. We show that having negative ties is associated with people being more peripheral within their subgroups, but closer to other groups within a population, which can have the effect of bringing the whole population closer together. Furthermore, at the collective level, information diffusion is facilitated, and polarization is reduced, by the presence of negative ties. Negative ties can be constructive.

The relationship of the number of negative ties (undirected, inbound, and outbound) with four topological characteristics: 1) average geodesic distancefrom all other nodes in the giant component of the network; 2) average geodesic distance of a node’s neighbors from all other nodes in the giant component of the network; 3) local clustering coefficient; and 4) betweenness centrality. The predictions estimating the average marginal effect for the number of negative ties. As indicated, nodes that have more negative ties are situated on the outskirts of the communities, acting as bridges. These nodes show smaller clustering coefficients, larger betweenness centralities, and smaller average geodesic distances to other nodes in the network. Furthermore, nodes with higher negative connections have neighboring nodes that exhibit a smaller geodesic distance from other nodes in the network, on average. This indicates that the neighbors of nodes with higher negative degrees are more globally dispersed compared to nodes with lower negative degrees that are more locally dispersed.

References

2024

  1. PNAS-04c.png
    The structure and function of antagonistic ties in village social networks
    Amir Ghasemian, and Nicholas A Christakis
    Proceedings of the National Academy of Sciences, 2024
    Image courtesy of Cavan Huang