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07/15/2023
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing atten...
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07/15/2023
MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
Graph Neural Networks (GNNs) have received increasing attention due to t...
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07/04/2023
Random Walk on Multiple Networks
Random Walk is a basic algorithm to explore the structure of networks, w...
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03/21/2023
Time Series Contrastive Learning with Information-Aware Augmentations
Various contrastive learning approaches have been proposed in recent yea...
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01/07/2023
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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12/16/2022
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Graph serves as a powerful tool for modeling data that has an underlying...
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10/26/2022
Personalized Federated Learning via Heterogeneous Modular Networks
Personalized Federated Learning (PFL) which collaboratively trains a fed...
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05/27/2022
On Consistency in Graph Neural Network Interpretation
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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10/28/2021
InfoGCL: Information-Aware Graph Contrastive Learning
Various graph contrastive learning models have been proposed to improve ...
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03/26/2021
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techni...
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11/13/2020
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Graph Neural Networks (GNNs) have shown to be powerful tools for graph a...
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11/09/2020
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating v...
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11/09/2020
