On the Worst-Case Performance of the Monte Carlo Method for Incremental Pagerank

Details On the Worst-Case Performance of the Monte Carlo Method for Incremental Pagerank On the Worst-Case Performance of the Monte Carlo Method for Incremental Pagerank

2012-04-24

arxiv.org/abs/1204.5500v1

Computer Science

Data Structures and Algorithms

Scientific paper

This note extends the analysis of incremental PageRank in [B. Bahmani, A. Chowdhury, and A. Goel. Fast Incremental and Personalized PageRank. VLDB 2011]. In that work, the authors prove a running time of $O(\frac{nR}{\epsilon^2} \ln(m))$ to keep PageRank updated over $m$ edge arrivals in a graph with $n$ nodes when the algorithm stores $R$ random walks per node and the PageRank teleport probability is $\epsilon$. To prove this running time, they assume that edges arrive in a random order, and leave it to future work to extend their running time guarantees to adversarial edge arrival. In this note, we show that the random edge order assumption is necessary by exhibiting a graph and adversarial edge arrival order in which the running time is $\Omega (R n^{1 + lg{3/2(1-\epsilon)}})$.

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