Area of Interest:Social Network Analysis, Information Diffusion, Influence Maximization, Immunization, Recommendation Systems, Link Prediction, Graph Embedding
Influence Maximization with a Single Cascade
The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. We have proposed a unified model which provides an approximate analytical solution to the problem of predicting the probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We have built upon our unified model to develop an efficient method for influence maximization OSSUM.