Reviews: Optimal Tagging with Markov Chain Optimization

Neural Information Processing Systems 

Optimization of the link structure for PR is not a new topic. Apart from papers mentioned in Related work, there are also those not reviewed, including "PageRank Optimization by Edge Selection" by Csaji et al., "Maximizing PageRank with New Backlinks" by Olsen, "PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation" by Csaji et al. **The novelty** of the study is questionable. The probability of reaching the target state $\sigma$ can be viewed as the state's stationary probability for the graph, where the added edges are directed to the state $\sigma$ and the matrix of transition probabilities is raised to an appropriate power. This observation does not immediately reduce the problem of the paper to a known task, however, it may partially explain the similarity between the theoretical part and the works of Olsen, where the stationary probability is maximized. In particular, Section 4 resembles the work "Maximizing PageRank with New Backlinks" (not cited in the paper), where M. Olsen considered a reduction of a Markov chain optimization problem to the independent set problem, which is equivalent to the vertex cover problem. Theorems 5.1, 5.3 are reasonable, but very simple and resemble Lemmas 1,2 from [15].