In recent years, companies have started to utilise online social communities as a means of communicating with and targeting their employees and customers, and such online communities include discussion fora. The conversational dynamics of users in fora can influence their neighbours in the underlying social network. We make use of such influence to target specific communities with information, i.e. post in them, because a post is generally shared with the community and not just with individual users. In short, we study information diffusion across communities and show that we can achieve high community (and user) spread using an efficient targeting strategy. In order to achieve this, we use a set of novel measures for cross-community influence and show that it outperforms other targeting strategies on two different data-sets: the largest Irish online discussion system, Boards.ie, and technical support fora, SAP SCN.

In recent years, the summarisation and decomposition of social networks has become increasingly popular, from community finding to role equivalence. However, these approaches concentrate on one type of model only. Generalised blockmodelling decomposes a network into independent, interpretable, labeled blocks, where the block labels summarise the relationship between two sets of users. Existing algorithms for fitting generalised blockmodels do not scale beyond networks of 100 vertices. In this paper, we introduce two new algorithms, one based on genetic algorithms and the other on simulated annealing, that is at least two orders of magnitude faster than existing algorithms and obtaining similar accuracy. Using synthetic and real datasets, we demonstrate their efficiency and accuracy and show how generalised blockmodelling and our new approaches enable tractable network summarisation and modelling of medium sized networks. 1