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The Relative Contribution of Sex and Drug Ties to STI-relevant Network Connectivity. James Moody & jimi adams Duke & Ohio State University . Sunbelt XXVI – Annual Meetings of the International Network for Social Network Analysis – Vancouver, BC
Duke & Ohio State University
Sunbelt XXVI – Annual Meetings of the International Network for Social Network Analysis – Vancouver, BC
Thanks to Steve Muth, Martina Morris, John Potterat, Rich Rothenberg & the network modeling group at UW. Supported by NIH grants DA12831 and HD41877
Heterosexual networks cannot have closed triangles, which makes cliques impossible. This limits “recursion” and helps spread ties quickly to the wider population.
Drug-sharing ties allow closed triads, which are the building blocks for cliques. These are essential for building robust structures.
Connectivity & Tie Type
Structural constraints for connectivity by type of tie:
Can difference in the types of ties help explain race differences in STI rates?
While we know different types of ties facilitate the dyadic transmission of BBIs, there is less understanding of how different types of ties bridge population groups.
If race correlates with type of dyadic contact, and type of contact affects transmission rates, then we might get some purchase on the population heterogeneity of STI rates.
We approach this problem as a general question of network connectivity. How do different types of relations knit together a population?
We can assess the connectivity contributions of each type of tie by selectively removing ties from a network and assessing the change in a number of connectivity-relevant measures.
a) Select at random n ties of type k
b) calculate the connectivity measures on the resulting network
c) repeat this many times (here 500 at each setting).
We do this for Sex, Drug, Sex & Drug, and random ties, removing between 2% and 12% of the total ties observed in the network.
(Values in parentheses are QAP p-values for the relevant dyadic logit model)
Effect of Edge Removal on Size of the Giant Component
Effect of edge removal on Relative Size of the Largest Bicomponent
Relative size is (observed size) / (giant component size)
Effect of edge removal on Relative Average Distance Between Nodes
Relative distance is (observed distance) / (giant component size)
Effect of edge removal on Graph Transitivity Ratio
Effect of edge removal on Racial Segregation Index
Segregation is Freeman’s (1972) Segregation Index using a 4 category race variable
Across all connectivity measures, sex ties create the greatest extent.
Removing sex ties:
Quickly decreases the size of the largest component
Leaves the network with a relatively larger biconnected core
Increases average distance faster than drug or random ties
Increases the transitivity (redundancy) of the network
Increases the racial segregation of the network.
This suggests that, within the respondent sample, sex ties create “tendrils” that reach out into the wider population, but do so in a relatively sparse way, with (comparatively) fewer re-connections to the strongest core(s) of the network.
Relations that have both sex and drug content are often distinct from random, but not in a consistently “negative” manner. Here, there appear to act in a middling role.
Race-specific Contour distributions
Tie-specific edge distributions
Note the distribution of edge types. The large “eastern” cluster is where most of the sex is happening in this network.