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07/24/2023
Estimating Single-Node PageRank in Õ(min{d_t, √(m)}) Time
PageRank is a famous measure of graph centrality that has numerous appli...
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05/31/2023
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popu...
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05/30/2023
Optimal Dynamic Subset Sampling: Theory and Applications
We study the fundamental problem of sampling independent events, called ...
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05/14/2023
Decoupled Graph Neural Networks for Large Dynamic Graphs
Real-world graphs, such as social networks, financial transactions, and ...
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05/04/2023
On Range Summary Queries
We study the query version of the approximate heavy hitter and quantile ...
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03/24/2023
LONGNN: Spectral GNNs with Learnable Orthonormal Basis
In recent years, a plethora of spectral graph neural networks (GNN) meth...
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02/24/2023
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Polynomial filters, a kind of Graph Neural Networks, typically use a pre...
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02/14/2023
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
Molecular conformation generation (MCG) is a fundamental and important p...
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12/20/2022
Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme
Personalized PageRank (PPR) stands as a fundamental proximity measure in...
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10/29/2022
Clenshaw Graph Neural Networks
Graph Convolutional Networks (GCNs), which use a message-passing paradig...
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06/11/2022
Sampling-based Estimation of the Number of Distinct Values in Distributed Environment
In data mining, estimating the number of distinct values (NDV) is a fund...
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06/03/2022
Instant Graph Neural Networks for Dynamic Graphs
Graph Neural Networks (GNNs) have been widely used for modeling graph-st...
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05/27/2022
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Graph Neural Networks (GNNs) have received extensive research attention ...
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02/23/2022
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting
Transformer-based methods have shown great potential in long-term time s...
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02/06/2022
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Estimating the number of distinct values (NDV) in a column is useful for...
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02/04/2022
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
Designing spectral convolutional networks is a challenging problem in gr...
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01/31/2022
GSN: A Universal Graph Neural Network Inspired by Spring Network
The design of universal Graph Neural Networks (GNNs) that operate on bot...
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06/21/2021
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e.g., GPR-GNN and ChebyNet, a...
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03/10/2021
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common archit...
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01/09/2021
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Interactive response time is important in analytical pipelines for users...
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07/04/2020
Simple and Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a powerful deep learning approac...
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06/21/2020
Personalized PageRank to a Target Node, Revisited
Personalized PageRank (PPR) is a widely used node proximity measure in g...
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08/28/2019
Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries
Given a graph G, a source node s and a target node t, the personalized P...
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05/16/2019
Scalable Graph Embeddings via Sparse Transpose Proximities
Graph embedding learns low-dimensional representations for nodes in a gr...
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05/07/2019
PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
SimRank is a classic measure of the similarities of nodes in a graph. G...
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04/03/2019
Efficient Estimation of Heat Kernel PageRank for Local Clustering
Given an undirected graph G and a seed node s, the local clustering prob...
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09/20/2017