Efficient semi-supervised learning on locally informative multiple graphs

We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene networks, in which informative parts vary over networks. We present a powerful, time-efficient approach for this problem by combining soft spectral clustering with label propagation for multiple graphs. We demonstrate the effectiveness and efficiency of our approach using both synthetic and real biological datasets.

► Proposed semi-supervised learning on multiple graphs with informative subgraphs. ► Developed a powerful, time-efficient approach for this issue. ► Performed by combining soft spectral clustering with label propagation. ► Demonstrated effectiveness and efficiency using both synthetic and real data.