1.6k citations · 2.5k across the 36 of their papers we have counts for
5 papers · 1 filter
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Mingguo He, Zhewei Wei, Zengfeng Huang +1
Many representative graph neural networks, e.g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters. However, existing work either applies predefined f…
Approximate Graph Propagation
Hanzhi Wang, Mingguo He, Zhewei Wei +4
Efficient computation of node proximity queries such as transition probabilities, Personalized PageRank, and Katz are of fundamental importance in various graph mining and learning…
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang +6
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependenci…
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Shuyuan Yan, Bolin Ding, Wei Guo +4
Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecas…
Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push
Hao Wu, Junhao Gan, Zhewei Wei +1
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 nod…