PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning unknown graphs
Nicolò Cesa-Bianchi, Claudio Gentile and Fabio Vitale
In: The 20th International Conference on Algorithmic Learning Theory, 3-5 Oct 2009, Porto, Portugal.


Motivated by a problem of targeted advertising in social networks, we introduce and study a new model of online learning on labeled graphs where the graph is initially unknown, and the algorithm is free to choose the next vertex to predict. After observing that natural nonadaptive exploration/prediction strategies (like depth-first with majority vote) badly fail on simple binary labeled graphs, we introduce an adaptive strategy that performs well under the hypothesis that the vertices of the unknown graph (i.e., the members of the social network) can be partitioned into a few well-separated clusters within which labels are roughly constant (i.e., members in the same cluster tend to prefer the same products). Our algorithm is efficiently implementable and provably competitive against the best of these partitions.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5481
Deposited By:Fabio Vitale
Deposited On:21 January 2010