One way you could avoid this is to only use reliable nodes for storing and retrieving values. The reliability of a node will have to be computed by known-good nodes, and it could be something like the similarity of a node's last few computed ranking factors compared to the same ranking factors computed by known-good nodes (i.e. compare the node's scores for google.com to known-good scores for google.com). Using this approach, you'll need to avoid the "rogue reliable node" problem (for example, by using random checks or reducing all reliability scores randomly).
Another way you could approach this is to duplicate computation of ranking factors across multiple nodes, fetch all of the values at search time, and rank them on the client side (using variance, for example). You could also limit searches to sites that only have >10 duplicate values computed, so that there is some time before new sites are ranked. Additionally, any nodes with values outside of the normal range could be reported by the client in the background, and their reliability scores could be computed this way. This approach is time-consuming for the end user (unless you replicate known-good results to known-good nodes for faster lookups).
Also, take a look at this paper which describes a sybil-proof weak-trust system (which, as the authors explain, is more robust than the impossible sybil-proof strong-trust system): http://www.eecs.harvard.edu/econcs/pubs/Seuken_aamas14.pdf
