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06/21/2023
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
It has been discovered that Graph Convolutional Networks (GCNs) encounte...
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04/14/2023
Towards Controllable Diffusion Models via Reward-Guided Exploration
By formulating data samples' formation as a Markov denoising process, di...
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03/04/2023
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Many patients with chronic diseases resort to multiple medications to re...
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11/30/2022
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Graph neural networks (GNNs) are popular weapons for modeling relational...
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09/23/2022
Towards Complete-View and High-Level Pose-based Gait Recognition
The model-based gait recognition methods usually adopt the pedestrian wa...
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09/13/2022
MDM: Molecular Diffusion Model for 3D Molecule Generation
Molecule generation, especially generating 3D molecular geometries from ...
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06/23/2022
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
Graph instance contrastive learning has been proved as an effective task...
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05/23/2022
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction
How to accurately predict the properties of molecules is an essential pr...
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05/20/2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business ...
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05/12/2022
GPN: A Joint Structural Learning Framework for Graph Neural Networks
Graph neural networks (GNNs) have been applied into a variety of graph t...
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03/31/2022
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
As a powerful tool for modeling complex relationships, hypergraphs are g...
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03/15/2022
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
After the great success of Vision Transformer variants (ViTs) in compute...
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03/12/2022
Equivariant Graph Mechanics Networks with Constraints
Learning to reason about relations and dynamics over multiple interactin...
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03/03/2022
Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference
Learning set functions becomes increasingly more important in many appli...
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02/22/2022
Equivariant Graph Hierarchy-Based Neural Networks
Equivariant Graph neural Networks (EGNs) are powerful in characterizing ...
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02/17/2022
Transformer for Graphs: An Overview from Architecture Perspective
Recently, Transformer model, which has achieved great success in many ar...
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02/15/2022
Geometrically Equivariant Graph Neural Networks: A Survey
Many scientific problems require to process data in the form of geometri...
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01/25/2022
Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, is an essential component of online...
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01/24/2022
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery – A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to i...
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12/18/2021
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
As Graph Neural Networks (GNNs) are widely adopted in digital pathology,...
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09/08/2021
Local Augmentation for Graph Neural Networks
Data augmentation has been widely used in image data and linguistic data...
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06/14/2021
PI-GNN: A Novel Perspective on Semi-Supervised Node Classification against Noisy Labels
Semi-supervised node classification, as a fundamental problem in graph l...
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06/05/2021
Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations
Valuation problems, such as attribution-based feature interpretation, da...
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05/26/2021
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
With the success of the graph embedding model in both academic and indus...
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03/20/2021
Recognizing Predictive Substructures with Subgraph Information Bottleneck
The emergence of Graph Convolutional Network (GCN) has greatly boosted t...
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03/17/2021
Diversified Multiscale Graph Learning with Graph Self-Correction
Though the multiscale graph learning techniques have enabled advanced fe...
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11/10/2020
Deep Multimodal Fusion by Channel Exchanging
Deep multimodal fusion by using multiple sources of data for classificat...
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11/04/2020
On Self-Distilling Graph Neural Network
Recently, the teacher-student knowledge distillation framework has demon...
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10/12/2020
Graph Information Bottleneck for Subgraph Recognition
Given the input graph and its label/property, several key problems of gr...
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08/22/2020
Tackling Over-Smoothing for General Graph Convolutional Networks
Increasing the depth of Graph Convolutional Networks (GCN), which in pri...
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07/12/2020
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Graph Identification (GI) has long been researched in graph learning and...
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03/16/2020
Spectral Graph Attention Network
Variants of Graph Neural Networks (GNNs) for representation learning hav...
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02/04/2020
Graph Representation Learning via Graphical Mutual Information Maximization
The richness in the content of various information networks such as soci...
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01/17/2020
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
Social media has been developing rapidly in public due to its nature of ...
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10/01/2019
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
Molecule generation is to design new molecules with specific chemical pr...
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08/04/2019
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
With the great success of graph embedding model on both academic and ind...
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08/04/2019
The General Black-box Attack Method for Graph Neural Networks
With the great success of Graph Neural Networks (GNNs) towards represent...
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07/25/2019
DropEdge: Towards the Very Deep Graph Convolutional Networks for Node Classification
Existing Graph Convolutional Networks (GCNs) are shallow---the number of...
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07/25/2019
The Truly Deep Graph Convolutional Networks for Node Classification
Existing Graph Convolutional Networks (GCNs) are shallow---the number of...
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07/01/2019
Unsupervised Adversarial Graph Alignment with Graph Embedding
Graph alignment, also known as network alignment, is a fundamental task ...
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11/21/2018
