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Relationship and Sex Networks at Cornell

One of the questions posed to the class during lecture the first few days of class was to give an example of a network. Someone responded with social networks, such as who is friends with whom, and someone else responded with government networks of what country has alliances with what other country. As these answers were said, I was thinking of a very different network that was more pertinent to the Cornell community. That was a sex network. I don’t mean an underground network of prostitution or anything illegal like that, but more of the network of hookups and relationships. I was too embarrassed to shout out my suggestion in the middle of the 700 person lecture, but I was joking with my friends that this network of hook ups and relationships probably connected almost every student at Cornell.

This network would be defined by each person being a node and each relationship, whether a casual crush, simple hook up, or a full on relationship, would be an edge. This network can be analyzed further by adding strong and weak ties and positive and negative labels. A strong tie would be one where two individuals would be dating and a weak tie would be one where two individuals are just casually hooking up. Each strong or weak tie can then also be labeled as positive or negative. We can characterize positive and negative as the spread of disease, specifically STD’s. A positive strong tie would be one where two people are not carriers of an STD and are in a relationship, while a negative weak tie would be one where two people are carriers of an STD and are just hooking up.

Although this network might seem trivial, it has important applications for medicine, specifically to find the cause of the spread of disease, such as sexually transmitted diseases or diseases like Ebola, and try to stop them before they spread more. Research done by Keeling and Earnes at the University of Oxford in London describes the spread of STD’s specifically through network heterogeneities. They detail that the spread of disease tracked through the average number of neighbors, how clustered those neighbors are, and the infection status of those neighbors. The positive and negative connections on this network can also represent structural balance in a network of relationships at Cornell. There can be a group of people who are STD free and are connected by positive edges and weaker ties who are not related to another group of people who could be more sexually active.

http://www.pnas.org/content/99/20/13330.full

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