Within network learning on big graphs using secondary memory-based random walk kernels

Significant advances in high-throughput sequencing technologies raised exponentially the rate of acquisition of novel biological knowledge in the last decade, thus resulting in consistent difficulties in the analysis of vast amount of biological data. This adverse scenario is exacerbated by serious scalability limitations affecting state-of-the art within-network learning methods and by the limited availability of primary memory in off-the-shelf desktop computers. In this contribution we present the application of a novel graph kernel, transductive and secondary memory-based network learning algorithm able to effectively tackle the aforementioned limitations. The proposed algorithm is then evaluated on a large (more than 200,000 vertices) biological network using ordinary off-the-shelf computers. To our knowledge this is the first time a graph kernel learning method is applied to a so large biological network.