Master of Science (MS)
Social Network Models Influence
Influence maximization in social networks is the problem of selecting a limited
size of influential users as seed nodes so that the influence from these seed nodes can propagate to the largest number of other nodes in the network. Previous studies in influence maximization focused on three areas, i.e., designing propagation models, improving algorithms of seed-node selection and exploiting the structure of social networks. However, most of these studies ignored the time constraint in influence propagation. In this paper, I studied how to maximize influence propagation in a given time, i.e., maximizing the speed of influence propagation in social networks. I extended the classic Independent Cascade (IC) model to a Continuous Dynamic Extended Independent Cascade (CDE-IC) model. In addition, I propose a novel heuristic algorithm and evaluate the algorithm using two large academic collaboration data sets from www.arXiv.org. Comparing with previous classic heuristic algorithms on the CDE-IC model, the new algorithm is 9%-18% faster in influence propagation. Furthermore, I gave solution to calculate propagation probability between adjacent nodes by exploiting the structure of social networks.
Wang, Yubo, "MAXIMIZING THE SPEED OF INFLUENCE IN SOCIAL NETWORKS" (2015). Master's Projects. 393.