We found an algorithm that solves the edge case problem. Sinksource+ removes the negatives and adds one negative to every single node. The weight of the edge is also a constant, which means that it’s the same for a node of any degree. This significantly devalues nodes that aren’t very well connected and would not be suitable for polygenic analysis.
However, it works horrendously in a single layer method with no prime nodes. The AUC sticks at around 0.5, which is the worst possible AUC you can have. However, it works great in a two layer network, giving our SZ network a score of 0.65. If you add negatives back in with the addition of sinksource+ and reduce the sinksource+ constant, we get an SZ AUC of 0.675 and a cell motility AUC of 0.801, which is the best AUC we have seen for an SZ gene identifier. I’m also running the program on the autism positives right now, and it appears that the AUC is running at about similar levels to where they were with E1 and E2.