# Approximate Graph Propagation

@article{Wang2021ApproximateGP, title={Approximate Graph Propagation}, author={Hanzhi Wang and Mingguo He and Zhewei Wei and Sibo Wang and Ye Yuan and Xiaoyong Du and Ji-rong Wen}, journal={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining}, year={2021} }

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 particular, several recent works leverage fast node proximity computation to improve the scalability of Graph Neural Networks (GNN). However, prior studies on proximity computation and GNN feature propagation are on a case-by-case basis, with each paper focusing on a particular proximity measure. In this…

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## 6 Citations

SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

- Computer ScienceArXiv
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Theoretical analysis indicates that SCARA achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference, and it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M in 100 seconds.

Instant Graph Neural Networks for Dynamic Graphs

- Computer ScienceArXiv
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This paper proposes Instant Graph Neural Network (InstantGNN), an incremental computation approach for the graph representation matrix of dynamic graphs, set to work with dynamic graphs with the edge-arrival model, which avoids timeconsuming, repetitive computations and allows instant updates on the representation and instant predictions.

Accurate and Scalable Graph Neural Networks for Billion-Scale Graphs

- Computer Science2022 IEEE 38th International Conference on Data Engineering (ICDE)
- 2022

This paper proposes a novel scalable and effective GNN framework COSAL, which substitutes the expensive aggregation with an efficient proximate node selection mechanism, which picks out the most important nodes for each target node according to the graph topology, and proposes a fine-grained neighbor importance quantification strategy to enhance the expressive power of CosAL.

Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach

- Computer ScienceSIGMOD Conference
- 2022

This paper proposes several novel algorithms to efficiently compute the personalized PageRank vector with a decay factor α based on an interesting connection between the customized PageRank values and the weights of random spanning forests of the graph based on a newly-developed matrix forest theorem on graphs.

Learning Optimal Propagation for Graph Neural Networks

- Computer ScienceArXiv
- 2022

This paper proposes a bi-level optimization-based approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation matrix as well as the downstream semi-supervised node classiﬁcation simultaneously and explores a low-rank approximation model for further reducing the time complexity.

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