Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks
Abstract
We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goalis to estimate this global centrality measure having at disposal a limited amount of data. This is the case inmany real-world scenarios where data collection is expensive, the network is too big for data storage capacityor only partial information is available. The sampling algorithm is theoretically grounded by results derivedfrom spectral approximation theory. We studied the problemon both synthetic and real data and tested theperformance comparing with traditional methods, such as random walk and uniform sampling. We show thatapproximations obtained from such methods are not always reliable and that our algorithm, while preservingcomputational scalability, improves performance under different error measures.
Nicolò Ruggeri
PhD student
My research interests include, but are not limited to, Probabilistic Learning and Network Science, as well as connected fields. In particular, I aim at understanding how current probabilistic models can be improved upon, both on a representation and training level. I am also fascinated by how different ideas and concepts from within and outside ML interpolate in interesting and novel developments. Therefore, I strive to keep a broader view on theoretical and practical insights originating from different fields.
Caterina De Bacco
Associate Professor
My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.