Efficient Computation of Shapley Value for Centrality in Networks
Speaker: Assoc Professor Balaraman Ravindran, Department of Computer Science and Engineering at the Indian Institute of Technology Madras
![Balaraman Ravindran](/sites/default/files/styles/container_width_large_x1/public/Balaraman%20Ravindran.png?itok=sulNYhPX)
Date: Friday 9 May 2014
Time: 2.00pm to 3.00pm
Location: Blackfriars Campus, room CC05.GD.03
Seminar Chairman: Dr. Frank Jiang, Research Fellow, Advanced Analytics Institute (AAi), UTS
Abstract:
The Shapley Value is arguably the most important normative solution concept in coalitional games. One of its applications is in the domain of networks, where theShapley Value is used to measure the relative importance of individual nodes. This measure, which is called node centrality, is of paramount significance in many real-world application domains including social and organisational networks, biological networks, communication networks and the internet. Whereas computational aspects of the Shapley Value have been analyzed in the context of conventional coalitional games, this work presents the first such study of the Shapley Value for network centrality. Our results demonstrate that this particular application of the Shapley Value presents unique opportunities for efficiency gains. In particular, we develop exact analytical formulas for computing Shapley Value based centralities in both weighted and unweighted networks. These formulas not only provide an efficient (polynomial time) and error-free way of computing node centralities, but their surprisingly simple closed form expressions also offer intuition into why certain nodes are relatively more important to a network. Joint work with Karthik V. Aadithya and versions were reported in AAMAS ’10, WINE ’10, and the Journal of Artificial Intelligence (JAIR) ’13. The work was partly funded by Ericsson Research.
Short Biography of the Speaker:
Balaraman Ravindran is an associate professor at the Department of Computer Science and Engineering at the Indian Institute of Technology Madras. He completed his Ph.D. at the Department of Computer Science, University of Massachusetts, Amherst. He worked with Prof. Andrew G. Barto on an algebraic framework for abstraction in Reinforcement Learning.
His current research interests span the broader area of machine learning, ranging from Spatio-temporal Abstractions in Reinforcement Learning to social network analysis and Data/Text Mining. Much of the work in his group is directed toward understanding interactions and learning from them.
Overview to AAI seminar series
The Advanced Analytics Seminar Series presents the latest theoretical advancement and empirical experience in a broad range of interdisciplinary and business-oriented analytics fields. It covers topics related to data mining, machine learning, statistics, bioinformatics, behavior informatics, marketing analytics and multimedia analytics. It also provides a platform for the showcase of commercial products in ubiquitous advanced analytics. Speakers are invited from both academia and industry. It opens regularly on every Friday afternoon at the garden-like UTS Blackfriars Campus. You are warmly welcome to attend this seminar series.