No More Leaky PageRank

We have surveyed multiple PageRank implementations available with popular graph processing frameworks, and discovered that they treat sink vertices (i.e., vertices without outgoing edges) incorrectly. This leads to two issues: (i) incorrect PageRank scores, and (ii) flawed performance evaluations (as costly scatter operations are avoided). For synchronous PageRank implementations, a strategy to fix these issues exists (accumu-lating all values from sinks during an algorithmic superstep of a PageRank iteration), albeit with sizeable overhead. This solution, however, is not applicable in the context of asynchronous frameworks. We present and evaluate a novel, low-cost algorithmic solution to address this issue. For asynchronous PageRank, our key target, our solution simply requires an inexpensive O(Vertex) computation performed alongside the final normalization step. We also show that this strategy has advantages over prior work for synchronous PageRank, as it both avoids graph restructuring and reduces inline computation costs by performing a final score reassignment to vertices once at the end of processing.