Social Network Analytics
Bart Baesens · 2012
A social network consists of both nodes and edges. A node could be defined as a customer, household/ family, patient, etc. An edge could be defined as a friend relationship, a call, transmission, etc. Thus, social network can be represented as sociogram. This chapter also discusses social network learning, wherein the goal is within-network classification to compute the marginal class membership probability of a particular node given the other nodes in the network. Various important challenges arise when learning in social networks. A first key challenge is that the data are not independent and identically distributed (IID), an assumption often made in classical statistical models. Additionally, it is not easy to come up with a separation into a training set for model development and a test set for model validation, since the whole network is interconnected and cannot split into two parts. Moreover, many networks are huge in scale, and efficient computational procedures need to be developed to do the learning. Finally, one should not forget the traditional way of doing analytics using only node-specific information because this can still prove to be very valuable information for prediction as well.