HIV is a very serious infection that sees no race, no color, no gender, no economic background and not even a specific age group. It can affect anyone, at any time if they put themselves in a situation where they could be at risk. Internationally, 37 million people are living with HIV.

HIV has an extremely high incidence among homeless youth, as they are more likely to engage in high HIV-risk behaviors (e.g., unprotected sexual activity, injection drug use) than other sub-populations. In fact, previous studies show that homeless youth are at 16X greater risk of HIV infection than stably housed populations. Thus, any attempt at eradicating HIV crucially depends on our success at minimizing rates of HIV infection among homeless youth.

In order to reduce the transmission of HIV, we are using a peer leader social network-based intervention, for information dissemination about HIV.  Therefore, the key question is how can we identify the most “influential” youth, to be trained as peer leaders, from the social network of homeless youth? We want to find the youth who can spread awareness about HIV and induce behavior change among their peers, in the quickest and most efficient possible manner.

We, in our work, try to help homeless drop-in centers use their resources more effectively by modeling this entire problem of selecting the most influential youth as an Influence Maximization Problem, which is a widely studied problem in the field of Artificial Intelligence. However, most previous work in this area has failed at addressing some key challenges that show up in the real world. Two major challenges are:

1) Constructing social networks of homeless youth is a big challenge, since these youth are a hard-to-reach population, and mapping out their social circles requires a lot of time and money.  Their networks are constantly changing, and there is not a constant/consistent roster like a classroom of school children.

2) Even if we are able to construct these networks, there is always noise in the data collection procedure, which leads to uncertainty about the true structure of the social network. This uncertainty needs to be accounted before deciding who is “influential” in the social network and who isn’t.

To overcome these challenges, we use a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Decision Theory and Mathematics (that we developed in our lab). In order to find out more about our techniques and algorithms, please have a look at our publications.