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Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries [article]

Sibo Wang, Renchi Yang, Runhui Wang, Xiaokui Xiao, Zhewei Wei, Wenqing Lin, Yin Yang, Nan Tang
2019 arXiv   pre-print
Given a graph G, a source node s and a target node t, the personalized PageRank (PPR) of t with respect to s is the probability that a random walk starting from s terminates at t.  ...  However, PPR computation is known to be expensive on large graphs, and resistant to indexing.  ...  that their precisions for top-k PPR queries are the same as FORA on each dataset.  ... 
arXiv:1908.10583v1 fatcat:nvwhrwnbgvdvhd2tyr3zjqwvja

Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push [article]

Hao Wu, Junhao Gan, Zhewei Wei, Rui Zhang
2021 arXiv   pre-print
Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph.  ...  The Single-Source PPR (SSPPR) query computes the PPR's of all the nodes with respect to s on a directed graph G with n nodes and m edges, and it is an essential operation widely used in graph applications  ...  Topppr: top-k personalized pagerank queries with precision guar- Algorithms on Exact Personalized PageRank.  ... 
arXiv:2101.03652v2 fatcat:ietn6khm3jb2hm57ji7rswkltm

The Impact of Global Structural Information in Graph Neural Networks Applications [article]

Davide Buffelli, Fabio Vandin
2021 arXiv   pre-print
Inevitably, practical GNNs do not capture information depending on the global structure of the graph.  ...  Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.  ...  TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs.  ... 
arXiv:2006.03814v2 fatcat:xxm7fqiwtvbrjjxl5yoxctvs5q

Approximate Graph Propagation [article]

Hanzhi Wang, Mingguo He, Zhewei Wei, Sibo Wang, Ye Yuan, Xiaoyong Du, Ji-Rong Wen
2021 pre-print
Efficient computation of node proximity queries such as transition probabilities, Personalized PageRank, and Katz are of fundamental importance in various graph mining and learning tasks.  ...  In this paper, we propose Approximate Graph Propagation (AGP), a unified randomized algorithm that computes various proximity queries and GNN feature propagation, including transition probabilities, Personalized  ...  TopPPR [39] combines Forward Search, Monte Carlo, and Backward Search [31] to obtain a better error guarantee for top-𝑘 PPR estimation.  ... 
doi:10.1145/3447548.3467243 arXiv:2106.03058v1 fatcat:ukdwu6d7qffydobjiw73nstthi