Papapetrou, Panagiotis and Gionis, A. and Mannila, H. (2011) A Shapley value approach for influence attribution. In: Gunopulos, D. and Hofmann, T. and Malerba, D. and Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science 6912. Berlin, Germany: Springer Verlag, pp. 549-564. ISBN 9783642237836.
Abstract
Finding who and what is “important” is an ever-occurring question. Many methods that aim at characterizing important items or influential individuals have been developed in areas such as, bibliometrics, social-network analysis, link analysis, and web search. In this paper we study the problem of attributing influence scores to individuals who accomplish tasks in a collaborative manner. We assume that individuals build small teams, in different and diverse ways, in order to accomplish atomic tasks. For each task we are given an assessment of success or importance score, and the goal is to attribute those team-wise scores to the individuals. The challenge we face is that individuals in strong coalitions are favored against individuals in weaker coalitions, so the objective is to find fair attributions that account for such biasing. We propose an iterative algorithm for solving this problem that is based on the concept of Shapley value. The proposed method is applicable to a variety of scenarios, for example, attributing influence scores to scientists who collaborate in published articles, or employees of a company who participate in projects. Our method is evaluated on two real datasets: ISI Web of Science publication data and the Internet Movie Database.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 26 Jul 2013 14:12 |
| Last Modified: | 09 Aug 2023 12:34 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7858 |
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