Search in Social Networks: Designing Models and Algorithms That Maximize Human Influence

The ease with which people can use today´s technology to form connections has generated an unprecedented situation for society: an era of global connectivity. This ease of connecting has increased the number of people using social networks, making the search for connections in this kind of network extremely complex. In this paper, grounded in concepts from Network Science and Artificial Intelligence, we report on models we have constructed and on algorithms aimed at producing a search engine integrated into social networks environments. The contribution of this engine is its ability to evaluate the numerous paths that connect source and target people, opting for the path where the interpersonal influence through the path is maximized. This increases the probability of finding reliable connections. In order to simulate its operation, we implemented this search engine in a multiagent application whose test performance produced results that exceeded expectations.