  Personalalized PageRank uses random walks to determine the importance or
authority of nodes in a graph from the point of view of a given source node.
Much past work has considered how to compute personalized PageRank from a given
source node to other nodes. In this work we consider the problem of computing
personalized PageRanks to a given target node from all source nodes. This
problem can be interpreted as finding who supports the target or who is
interested in the target.
  We present an efficient algorithm for computing personalized PageRank to a
given target up to any given accuracy. We give a simple analysis of our
algorithm's running time in both the average case and the parameterized
worst-case. We show that for any graph with $n$ nodes and $m$ edges, if the
target node is randomly chosen and the teleport probability $\alpha$ is given,
the algorithm will compute a result with $\epsilon$ error in time
$O\left(\frac{1}{\alpha \epsilon} \left(\frac{m}{n} + \log(n)\right)\right)$.
This is much faster than the previously proposed method of computing
personalized PageRank separately from every source node, and it is comparable
to the cost of computing personalized PageRank from a single source. We present
results from experiments on the Twitter graph which show that the constant
factors in our running time analysis are small and our algorithm is efficient
in practice.
