Accelerated Local Algorithms for Personalized and Regularized PageRank

Local PageRank algorithms seek sparse approximations with work independent of graph size. We give a deterministic algorithm for regularized personalized PageRank with additive objective accuracy $\epsilon$ in $\tilde{\mathcal{O}}(1\mathrm{/}(\rho \sqrt{\alpha }))$ local work, where $\alpha$ is the lazy teleportation parameter and $\rho$ is the regularizer. Accuracy enters only polylogarithmically. The bound charges discovery, repeated neighborhood scans, numerical updates, certification, and output, without graph-wide preprocessing or a supplied solution support. The algorithm combines regularization continuation with accelerated corrections constrained by a degree-scaled box and a mass cap. Two energies for the same recurrence control objective convergence and the response that activates coordinates. A selected-flow argument bounds cumulative scanned volume, and a sparse threshold reporter realizes the bound. We also specify a bounded-arithmetic implementation for rational inputs. A second, randomized algorithm uses support-safe threshold batches. A block-Cholesky and Chebyshev argument bounds their depth, and certified SDD solves give expected work $\tilde{\mathcal{O}}(V_{\ast }\min \{k_{\ast },{\alpha }^{-1\mathrm{/}2}\})$, where $k_{\ast }$ and $V_{\ast }$ are the optimal support’s cardinality and degree volume. Both methods imply the corresponding accelerated degree-normalized PPR approximation. The concurrent September 2026 preprint of Cui, Wei, and Yang also attains the randomized work scale. Our principal distinction is deterministic local acceleration with only polylogarithmic overhead and no SDD oracle.