Framework. A) Insertion mutation data (blue lollipops, each occurring in one tumor) across a set of tumors (not shown) and four genomic regions. The region on Chr 5 and Chr 7 harbor sufficient mutations to be called CIS. The region on Chr 11 and Chr 16 do not. B) Gene mutation scores are obtained by weighted summarization. The weighing function is a flat-top Gaussian. C) PPI network with genes as nodes. Color denotes the mutation gene score. D) Illustration of the diffusion kernel. Conceptually, the diffusion kernel is similar to a heat diffusion process. In graphs this means that the mutation score at the graph node is diffused throughout the network dependent on the graph topology that connects this node to the rest of the network. Its global distance to the other nodes is thus dependent on the weight and number of paths connecting them as well as the diffusion strength. The latter parameter can be regarded as a scale parameter. For low diffusion strength, scores hardly diffuse and the interaction context of a gene is determined by itself and a few well-connected neighbors. For high diffusion strength, scores are almost fully diffused and can thus reach distant genes in the network, albeit in very small amounts. Using a permutation approach, it is possible to establish whether the diffused scores are higher than expected by chance (starred genes). Notably, genes with few or no mutations can still reach significance due to high scoring nodes in their neighborhood.