Speaker: Professor Qi Xingqin
Topic: Degree-like Centrality with structural zeroes or ones: When is a neighbor not a neighbor?
Date: December 2
Time: 16:00 pm
Venue: Room 1508, Building 9 (Tencent Meeting ID: 593522591)
Sponsor: School of Mathematics and Statistics, Institute of Science and Technology
In the field of social network analysis, identifying influential spreaders (or important vertices) is a significant procedure to understand, control or accelerate the dynamics of information (or disease) diffusion process in complex networks effectively. But there are situations in which researchers hope to ignore certain dyads in the computation of centrality to avoid biased or misleading results, while simply deleting these dyads will result in wrong conclusions. There is little work considering this particular problem except the eigenvector-like centrality method presented in 2015. In this work, were visit this problem and present a new degree-like centrality method which also allows some dyads to be excluded in the calculations. This new method adopts the technique of weighted symmetric nonnegative matrix factorization (abbreviatedas WSNMF), and we will show that it can be seen as the generalized version of the existing eigenvector-like centrality. After applying it to several datasets, we test this new method's efficiency.