Many graph algorithms benefit hugely from locality of reference, because graphs have an inherently relevant concept of things being affected by other things they are near. Taking advantage of locality of reference can make a difference of many orders of magnitude in speed, in my experience.
If you're trying to divide up the work by dividing the graph into parts that run on different nodes, where do you cut it? Even finding a good cut is a graph algorithm in itself. And any information that flows across that cut has to be serialized and deserialized, which is relatively quite slow.
Meanwhile, a single-node algorithm is usually hitting results that are in RAM or even in cache.
If you're trying to divide up the work by dividing the graph into parts that run on different nodes, where do you cut it? Even finding a good cut is a graph algorithm in itself. And any information that flows across that cut has to be serialized and deserialized, which is relatively quite slow.
Meanwhile, a single-node algorithm is usually hitting results that are in RAM or even in cache.