EpiRep: Learning Node Representations through Epidemic Dynamics on Networks

Understanding the dynamic properties of epidemic spreading on complex social networks is essential to make effective and efficient public health policies for epidemic prevention and control. In recent years, the concept of network embedding has attracted lots of attention to deal with various network analytic tasks, the purpose of which is to encode relationships or information of networked elements into a low-dimensional vector space. However, most existing embedding methods have focused mainly on preserving static network information, such as structural proximity, node/edge attributes, and labels. On the contrary, in this paper, we focus on the embedding problem of preserving dynamic characteristics of epidemic spreading on social networks. We propose a novel embedding method, namely EpiRep, to learn node representations of a network by maximizing the likelihood of preserving groups of infected nodes due to the epidemics starting from every single node on the network. Specifically, the Susceptible-Infectious model is adopted to simulate the epidemic dynamics on networks, and the Continuous Bag-of-Words model with negative sampling is used to obtain node representations. Experimental results show that the EpiRep method outperforms two benchmark random-walk based embedding methods in terms of node clustering and classification on several synthetic and real-world networks. The proposed method and findings in this paper may offer new insight for source identification and infection prevention in the face of epidemic spreading on social networks.