Thus, in order to obtain contact network data that are relevant for the spread of infectious diseases transmitted directly from person to person by the respiratory or close-contact route, we make the following assumptions: Individuals who have a friendship relation in the network, and who either (a) have the same dormitory residence, or (b) who major in the same field and the same class year, are likely to be in close enough physical contact on a regular basis as to be able to transmit an infection to each other. Thus, using the raw friendship data and the available information on dormitory residence, major, and class year, we extract the subgraph which reflects our assumptions. Having extracted the subgraph, we remove all nodes who are not part of the largest connected component (i.e. small subgraphs that are not part of the larger network). The networks thus reduce to the following contact networks: We note that the modularity Q of these networks is within the range of modularities measured in the computationally generated networks (see for example Figure 1) , with the exception of one network (Georgetown). Clearly, these networks will contain contacts that are not relevant for the spread of diseases (false positives) -at the same time, they will also miss some relevant contacts (false negatives). However, given the accuracy and amount of data, these networks are well suited to study the spread of infectious diseases on human contact networks, in particular for diseases transmitted directly from person to person by the respiratory or close-contact route. Degree distributions of these networks are shown in Suppl. Mat. Figure S2 .