PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Shapley-value Approach for Influence Attribution
Panagiotis Papapetrou, Aris Gionis and Heikki Mannila
European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases pp. 549-565, 2011.


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, bibliomet- rics, 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 im- portance 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 con- cept 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 partic- ipate in projects. Our method is evaluated on two real datasets: ISI Web of Science publication data and the Internet Movie Database.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8923
Deposited By:Panagiotis Papapetrou
Deposited On:21 February 2012