Disadvantage of diffusion model

Unfortunately, knowledge of the underlying diffusion model and its parameters is essential for the existing IM algorithms to perform well. For example, Du et al. (2014) empirically showed that misspecification of the diffusion model can lead to choosing bad seeds and consequently to a low spread. In practice, it is not clear how to choose from amongst the increasing number of plausible diffusion models (Kempe et al., 2003; Gomez Rodriguez et al., 2012; Li et al., 2013). Even if we are able to choose a diffusion model according to some prior information, the number of parameters for these models scales with the size of the network (for example, it is equal to the number of edges for both the IC and LT models) and it is not clear how to set these. Goyal et al. (2011a) showed that even when assuming the IC or LT model, correct knowledge of the model parameters is critical to choosing good seeds that lead to a large spread. Some papers try to learn these parameters from past propagation data (Saito et al., 2008; Goyal et al., 2010; Netrapalli & Sanghavi, 2012). However in practice, such data is hard to obtain and the large number of parameters makes this learning challenging.