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04/24/2023
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) as an emerging technique hav...
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04/02/2023
Graph Mining for Cybersecurity: A Survey
The explosive growth of cyber attacks nowadays, such as malware, spam, a...
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04/02/2023
Abnormal Event Detection via Hypergraph Contrastive Learning
Abnormal event detection, which refers to mining unusual interactions am...
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03/02/2023
Specformer: Spectral Graph Neural Networks Meet Transformers
Spectral graph neural networks (GNNs) learn graph representations via sp...
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12/14/2022
MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning
Contrastive learning (CL), which can extract the information shared betw...
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11/30/2022
Directed Acyclic Graph Structure Learning from Dynamic Graphs
Estimating the structure of directed acyclic graphs (DAGs) of features (...
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11/30/2022
xTrimoABFold: Improving Antibody Structure Prediction without Multiple Sequence Alignments
In the field of antibody engineering, an essential task is to design a n...
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10/06/2022
Uncovering the Structural Fairness in Graph Contrastive Learning
Recent studies show that graph convolutional network (GCN) often perform...
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10/05/2022
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Graph Contrastive Learning (GCL), learning the node representations by a...
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09/28/2022
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by...
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05/17/2022
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Knowledge representation learning has been commonly adopted to incorpora...
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05/08/2022
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Graph neural networks (GNNs) have been widely used in modeling graph str...
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03/01/2022
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal gr...
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02/18/2022
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed...
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01/27/2022
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Graph Convolutional Networks (GCNs) have recently attracted vast interes...
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01/19/2022
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Most existing Graph Neural Networks (GNNs) are proposed without consider...
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01/14/2022
Compact Graph Structure Learning via Mutual Information Compression
Graph Structure Learning (GSL) recently has attracted considerable atten...
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11/20/2021
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Graph Neural Networks (GNNs) are proposed without considering the agnost...
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09/29/2021
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy, ...
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05/19/2021
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Heterogeneous graph neural networks (HGNNs) as an emerging technique hav...
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04/15/2021
Lorentzian Graph Convolutional Networks
Graph convolutional networks (GCNs) have received considerable research ...
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03/04/2021
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
Semi-supervised learning on graphs is an important problem in the machin...
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01/28/2021
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Graph Neural Networks (GNNs) have received considerable attention on gra...
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01/04/2021
Beyond Low-frequency Information in Graph Convolutional Networks
Graph neural networks (GNNs) have been proven to be effective in various...
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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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11/30/2020
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information netwo...
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10/06/2020
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Inference on a large-scale knowledge graph (KG) is of great importance f...
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09/02/2020
Heterogeneous Graph Neural Network for Recommendation
The prosperous development of e-commerce has spawned diverse recommendat...
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07/05/2020
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have gained great popularity in tack...
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06/29/2020
Decorrelated Clustering with Data Selection Bias
Most of existing clustering algorithms are proposed without considering ...
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02/05/2020
Structural Deep Clustering Network
Clustering is a fundamental task in data analysis. Recently, deep cluste...
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12/06/2019
Hyperbolic Graph Attention Network
Graph neural network (GNN) has shown superior performance in dealing wit...
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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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10/30/2019
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
With the information explosion of news articles, personalized news recom...
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09/14/2019
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Recently, recommender systems play a pivotal role in alleviating the pro...
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09/10/2019
Temporal Network Embedding with Micro- and Macro-dynamics
Network embedding aims to embed nodes into a low-dimensional space, whil...
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05/15/2019
