PageRank Optimization by Edge Selection

Abstract

The importance of a node in a directed graph can be measured by its PageRank. The PageRank of a node is used in a number of application contexts - including ranking websites - and can be interpreted as the average portion of time spent at the node by an infinite random walk. We consider the problem of maximizing the PageRank of a node by selecting some of the edges from a set of edges that are under our control. By applying results from Markov decision theory, we show that an optimal solution to this problem can be found in polynomial time. Our core solution results in a linear programming formulation, but we also provide an alternative greedy algorithm, a variant of policy iteration, which runs in polynomial time, as well. Finally, we show that, under the slight modification for which we are given mutually exclusive pairs of edges, the problem of PageRank optimization becomes NP-hard.

30 pages, 3 figures

References in corpus (2)

• On the Complexity of Solving Markov Decision Problems
• Ergodic Control and Polyhedral approaches to PageRank Optimization

Cited by in corpus (11)

• PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
• A Web Aggregation Approach for Distributed Randomized PageRank Algorithms
• Ergodic Control and Polyhedral approaches to PageRank Optimization
• Katz Centrality of Markovian Temporal Networks: Analysis and Optimization
• Scalable Attack on Graph Data by Injecting Vicious Nodes
• The robustness of democratic consensus
• Policy Iteration is well suited to optimize PageRank
• The PageRank Problem, Multi-Agent Consensus and Web Aggregation -- A Systems and Control Viewpoint
• Ergodic Randomized Algorithms and Dynamics over Networks
• Efficient PageRank Computation via Distributed Algorithms with Web Clustering
• Fully personalized PageRank and algebraic methods to distribute a random walker