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Found 26 papers, 18 papers with code
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational demands.
Ranked #185 on
Image Classification
on ImageNet
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
These spatial-based HGNNs neglect the utilization of spectral graph convolutions, which are the foundation of Graph Convolutional Networks (GCN) on homogeneous graphs.
Ranked #5 on
Node Property Prediction
on ogbn-mag
Decoupled Graph Neural Networks for Large Dynamic Graphs
The experimental results demonstrate that our algorithm achieves state-of-the-art performance in both kinds of dynamic graphs.
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks.
Graph Neural Networks with Learnable and Optimal Polynomial Bases
Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal.
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks.
Clenshaw Graph Neural Networks
Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations.
Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling
The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research.
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data.
Ranked #1 on
Molecular Property Prediction
on MUV
Instant Graph Neural Networks for Dynamic Graphs
With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes.
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels.
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting
In this way, if a key segment has a high correlation score with the query segment, its successive segment contributes more to the prediction of the query segment.
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
We derive conditions of the learning framework under which the learned model is workload agnostic, in the sense that the model/estimator can be trained with synthetically generated training data, and then deployed into any data warehouse simply as, e. g., user-defined functions (UDFs), to offer efficient (within microseconds on CPU) and accurate NDV estimations for unseen tables and workloads.
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
GPR-GNN and BernNet demonstrate that the Monomial and Bernstein bases also outperform the Chebyshev basis in terms of learning the spectral graph convolutions.
MGNN: Graph Neural Networks Inspired by Distance Geometry Problem
As for spatial GNNs, models like Graph Isomorphism Networks (GIN) analyze their expressive power based on Graph Isomorphism Tests.
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting
Long-term time series forecasting is widely used in real-world applications such as financial investment, electricity management and production planning.
Coarformer: Transformer for large graph via graph coarsening
We get rid of these obstacles by exploiting the complementary natures of GNN and Transformer, and trade the fine-grained long-range information for the efficiency of Transformer.
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Many representative graph neural networks, e. g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters.
GPR
Node Classification on Non-Homophilic (Heterophilic) Graphs
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are.
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
Simple and Deep Graph Convolutional Networks
We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
Scalable Graph Embeddings via Sparse Transpose Proximities
Based on the concept of transpose proximity, we design \strap, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously.
