Transductive Learning over Graphs : Incremental Assessment

Graphs constitute a most natural way to represent problems involving finite or countable universes. This might be especially so in the context of bio-informatics (e.g. for protein-interaction graphs), collaborative filtering, the analysis of social networks and citation graphs, and to various problems in operations research in the context of incomplete information. A further argument for using graphs for characterizing learning problems was found in the connection it makes to the literature on network flow algorithms and other deep results of combinatorial optimization problems.