If one defines a graph with each individual representing a node, and an edge connecting two individuals who have had sexual contact, then the large majority are part of a huge connected cluster.
Here is a paper which observes this in a high school. Here is just the graph. An animated gif of the development over time. One thing that disturbs me about the paper is that they make no mention of asymmetric claims by the students. (ETA: actually, they did, see cupholder)
That is a fascinating paper, and engagingly written. I think the most surprising thing from it was that this weird almost-a-spanning-tree structure arises from two simple, local rules:
People tend to date other people with a similar amount of past sexual experience.
People avoid dating the exes of other people who are close to them in the relationship graph, since this makes them look bad to their friends, exes, and so on. This accounts for the lack of short cycles.
When the authors applied these rules in a computer simulation, they ended up with results almost indistinguishable from the empirically-observed sex-graphs.
Well, there is a brief mention tucked away in a footnote:
In fig. 2, and in all discussions presented here, all romantic and sexual relationship nominations linking students are included, whether or not the nomination from i to j was reciprocated with a nomination from j to i.
Not that it’s very reassuring; I didn’t see any data on how many/what proportion of claimed relationships were asymmetric.
The relationships in that high school are similar but not necessarily analogous to a polyamorous network. Because the relationships that make up that graph don’t overlap temporally at their connecting nodes, an STD that enters the graph can only affect people that form a new connection after its appearance. An STD in a polyamorous network can spread to every member, regardless of when they join the network.
That’s kinda bad. Poly folk tend to be very concerned about STDs; common best-practices are to use barriers with new partners (or all partners), get tested for new infections regularly (usually monthly), and to require one’s partners to do the same.
This lines up pretty closely with Daniel’s recommendation, but even if you take every precaution imaginable, being a part of a large polyamorous network will increase your risk of exposure by at least a little.
Though it may be worth mentioning that that effect may be offset by the generally high level of caution in the poly community and increased certainty about your partner’s partners, what with cheating being (almost entirely) out of the equation.
Poly folk tend to be very concerned about STDs; common best-practices are to use barriers with new partners (or all partners), get tested for new infections regularly (usually monthly), and to require one’s partners to do the same.
OK, but IMHO there is significant risk from infectious agents for which we do not yet have reliable affordable tests or that we do not yet believe to be sexually transmitted or that we do not yet believe to be particularly harmful.
(The spirochete that causes Lyme disease would be an agent of the second category.)
Here is a paper which observes this in a high school. Here is just the graph. An animated gif of the development over time. One thing that disturbs me about the paper is that they make no mention of asymmetric claims by the students. (ETA: actually, they did, see cupholder)
That is a fascinating paper, and engagingly written. I think the most surprising thing from it was that this weird almost-a-spanning-tree structure arises from two simple, local rules:
People tend to date other people with a similar amount of past sexual experience.
People avoid dating the exes of other people who are close to them in the relationship graph, since this makes them look bad to their friends, exes, and so on. This accounts for the lack of short cycles.
When the authors applied these rules in a computer simulation, they ended up with results almost indistinguishable from the empirically-observed sex-graphs.
Well, there is a brief mention tucked away in a footnote:
Not that it’s very reassuring; I didn’t see any data on how many/what proportion of claimed relationships were asymmetric.
Thanks!
The relationships in that high school are similar but not necessarily analogous to a polyamorous network. Because the relationships that make up that graph don’t overlap temporally at their connecting nodes, an STD that enters the graph can only affect people that form a new connection after its appearance. An STD in a polyamorous network can spread to every member, regardless of when they join the network.
That’s kinda bad. Poly folk tend to be very concerned about STDs; common best-practices are to use barriers with new partners (or all partners), get tested for new infections regularly (usually monthly), and to require one’s partners to do the same.
This lines up pretty closely with Daniel’s recommendation, but even if you take every precaution imaginable, being a part of a large polyamorous network will increase your risk of exposure by at least a little.
Though it may be worth mentioning that that effect may be offset by the generally high level of caution in the poly community and increased certainty about your partner’s partners, what with cheating being (almost entirely) out of the equation.
OK, but IMHO there is significant risk from infectious agents for which we do not yet have reliable affordable tests or that we do not yet believe to be sexually transmitted or that we do not yet believe to be particularly harmful.
(The spirochete that causes Lyme disease would be an agent of the second category.)
For scale, on that graph are shown relationships between 573 students in a population of ~1000 students, of whom 832 were interviewed.