Search Results for author:
Found 26 papers, 11 papers with code
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
On Structural Expressive Power of Graph Transformers
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed.
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
Hierarchical Transformer for Scalable Graph Learning
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning.
LEReg: Empower Graph Neural Networks with Local Energy Regularization
With Intra-Energy Reg, we strengthen the message passing within each part, which is beneficial for getting more useful information.
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
These experiments show the excellent expressive power of MWGNN in dealing with graph data with various distributions.
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.
Equivalent Distance Geometry Error for Molecular Conformation Comparison
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.
Ranked #8 on
Traffic Prediction
on PeMS07
(using extra training data)
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Lorentzian Graph Convolutional Networks
We also find that the performance of some hyperbolic GCNs can be improved by simply replacing the graph operations with those we defined in this paper.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
However, the inability of GNNs to model substructures in graphs remains a significant drawback.
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).
Learning Node Representations from Noisy Graph Structures
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
EPNE: Evolutionary Pattern Preserving Network Embedding
In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.
Streaming Graph Neural Networks via Continual Learning
In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.
Graph Structural-topic Neural Network
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.
GraLSP: Graph Neural Networks with Local Structural Patterns
Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem.
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
Tag2Vec: Learning Tag Representations in Tag Networks
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes.
SepNE: Bringing Separability to Network Embedding
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing.
