Costantini, S. and De Meo, P. and Giorgianni, A. and Migliorato, V. and Provetti, Alessandro and Salvia, F. (2020) Exploring low-degree nodes first accelerates network exploration. In: 12th ACM Web Science Conference 2020, 6-10 July 2020, Southampton, UK. (In Press)
|
Text
costantini-exploring_low_degree-WebSci20.pdf - Author's Accepted Manuscript Download (3MB) | Preview |
Abstract
We consider information diffusion on Web-like networks and how random walks can simulate it. A well-studied problem in this domain is Partial Cover Time, i.e., the calculation of the expected number of steps a random walker needs to visit a given fraction of the nodes of the network. We notice that some of the fastest solutions in fact require that nodes have perfect knowledge of the degree distribution of their neighbors, which in many practical cases is not obtainable, e.g., for privacy reasons. We thus introduce a version of the Cover problem that considers such limitations: Partial Cover Time with Budget. The budget is a limit on the number of neighbors that can be inspected for their degree; we have adapted optimal random walks strategies from the literature to operate under such budget. Our solution is called Min-degree (MD) and, essentially, it biases random walkers towards visiting peripheral areas of the network first. Extensive benchmarking on six real datasets proves that the---perhaps counter-intuitive strategy---MD strategy is in fact highly competitive wrt. state-of-the-art algorithms for cover.
Metadata
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Also available on arXiv. |
| Keyword(s) / Subject(s): | According to ACM 2012 classification: - I.2.8: Problem Solving, Control Methods, and Search According to arXiv classification: - Social and Information Networks (cs.SI) - Data Structures and Algorithms (cs.DS) |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Data Analytics, Birkbeck Institute for |
| Depositing User: | Alessandro Provetti |
| Date Deposited: | 21 May 2020 13:30 |
| Last Modified: | 09 Aug 2023 12:48 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/31937 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
