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05/23/2023
Continual Learning on Dynamic Graphs via Parameter Isolation
Many real-world graph learning tasks require handling dynamic graphs whe...
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03/20/2022
LEReg: Empower Graph Neural Networks with Local Energy Regularization
Researches on analyzing graphs with Graph Neural Networks (GNNs) have be...
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12/26/2021
Stepping Back to SMILES Transformers for Fast Molecular Representation Inference
In the intersection of molecular science and deep learning, tasks like v...
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06/24/2021
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
Spatial-temporal forecasting has attracted tremendous attention in a wid...
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05/08/2021
HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
Well-designed molecular representations (fingerprints) are vital to comb...
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04/15/2021
Lorentzian Graph Convolutional Networks
Graph convolutional networks (GCNs) have received considerable research ...
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04/07/2021
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
Graph neural networks (GNNs) have achieved tremendous success in graph m...
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12/04/2020
Learning Node Representations from Noisy Graph Structures
Learning low-dimensional representations on graphs has proved to be effe...
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09/24/2020
EPNE: Evolutionary Pattern Preserving Network Embedding
Information networks are ubiquitous and are ideal for modeling relationa...
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09/23/2020
Streaming Graph Neural Networks via Continual Learning
Graph neural networks (GNNs) have achieved strong performance in various...
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06/25/2020
Graph Structural-topic Neural Network
Graph Convolutional Networks (GCNs) achieved tremendous success by effec...
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11/25/2019
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be natur...
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11/18/2019
GraLSP: Graph Neural Networks with Local Structural Patterns
It is not until recently that graph neural networks (GNNs) are adopted t...
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11/11/2019
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however, ...
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06/03/2019
DANE: Domain Adaptive Network Embedding
Recent works reveal that network embedding techniques enable many machin...
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02/26/2019
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proved to be a most powerful ar...
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11/14/2018
