This paper focuses on multi-label learning from small amounts of labelled data. We demonstrate that the binary-relevance extension of the interpolated label propagation algorithm, the harmonic function, is a competitive learning method with respect to many widely-used evaluation measures. This is achieved by a new transition matrix that better captures the underlying structure useful for classification coupled with the use of data dependent thresholding strategies. Furthermore, we show that in the case of label dependence, one can use the outputs of a competitive learning model as part of the input to the harmonic function to improve the performance of this model. Finally, since we are using multiple measures to thoroughly evaluate the performance of the algorithm, we propose to use the game-theory based method of Kalai and Smorodinsky to output a single compromise solution for all measures. This method can be applied to any learning model irrespective of the number of evaluation metrics used.