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Cross-Domain Graph Anomaly Detection

IEEE transaction on neural networks and learning systems, 2022-06, Vol.33 (6), p.2406-2415

2022

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Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity

IEEE transaction on neural networks and learning systems, 2024-09, Vol.35 (9), p.11661-11670

2024

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Data‐efficient graph learning: Problems, progress, and prospects

The AI magazine, 2024-10

2024

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Combating disinformation in a social media age

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2020-11, Vol.10 (6), p.e1385-n/a

2020

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Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification

ACM transactions on knowledge discovery from data, 2024-02, Vol.18 (4), p.1-18, Article 83

2024

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Data-Efficient Graph Learning

Ding, Kaize

2023

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Generalized few-shot node classification: toward an uncertainty-based solution

Knowledge and information systems, 2024-02, Vol.66 (2), p.1205-1229

2024

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MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, p.3291-3301

2024

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Data-Efficient Graph Learning

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2024, Vol.38 (20), p.22663-22663

Ding, Kaize

2024

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Next-item Recommendation with Sequential Hypergraphs

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, p.1101-1110

2020

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IOX1 protects from TGF‑β induced fibrosis in LX‑2 cells via the regulation of extracellular matrix protein expression

Experimental and therapeutic medicine, 2021-03, Vol.21 (3), Article 180

2021

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Few-shot Network Anomaly Detection via Cross-network Meta-learning

Proceedings of the Web Conference 2021, 2021, p.2448-2456

2021

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Interactive Anomaly Detection on Attributed Networks

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019, p.357-365

2019

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Supervised Graph Contrastive Learning for Few-Shot Node Classification

Machine Learning and Knowledge Discovery in Databases, 2023, p.394-411

2023

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Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, p.295-304

2020

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AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, p.392-401

2021

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Causal Disentanglement with Network Information for Debiased Recommendations

Similarity Search and Applications, 2022, Vol.13590, p.265-273

2022

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Sequential Recommendation for Cold-start Users with Meta Transitional Learning

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, p.1783-1787

2021

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Graph Few-shot Learning with Attribute Matching

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, p.1545-1554

2020

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Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph

Social, Cultural, and Behavioral Modeling, 2022, Vol.13558, p.35-45

2022

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Graph Few-shot Class-incremental Learning

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022, p.987-996

2022

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Federated Few-shot Learning

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, p.2374-2385

2023

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GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023, p.339-347

2023

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MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection

2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA), 2024, p.1-10

2024

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Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, p.574-584

2024

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Cross-Domain Graph Anomaly Detection

Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity

Data‐efficient graph learning: Problems, progress, and prospects

Combating disinformation in a social media age

Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification

Data-Efficient Graph Learning

Generalized few-shot node classification: toward an uncertainty-based solution

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

Data-Efficient Graph Learning

Next-item Recommendation with Sequential Hypergraphs

IOX1 protects from TGF‑β induced fibrosis in LX‑2 cells via the regulation of extracellular matrix protein expression

Few-shot Network Anomaly Detection via Cross-network Meta-learning

Interactive Anomaly Detection on Attributed Networks

Supervised Graph Contrastive Learning for Few-Shot Node Classification

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter

Causal Disentanglement with Network Information for Debiased Recommendations

Sequential Recommendation for Cold-start Users with Meta Transitional Learning

Graph Few-shot Learning with Attribute Matching

Classifying COVID-19 Related Meta Ads Using Discourse Representation Through a Hypergraph

Graph Few-shot Class-incremental Learning

Federated Few-shot Learning

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection

Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise

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