Jintang Li

Orcid: 0000-0002-6405-1531

According to our database¹, Jintang Li authored at least 39 papers between 2020 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

FairAGG: Toward Fair Graph Neural Networks via Fair Aggregation.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Soc. Syst., October, 2024

Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective.

[BibT_eX]

[DOI]

CoRR, 2024

L<sup>2</sup>CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering.

[BibT_eX]

[DOI]

CoRR, 2024

State Space Models on Temporal Graphs: A First-Principles Study.

[BibT_eX]

[DOI]

CoRR, 2024

SGHormer: An Energy-Saving Graph Transformer Driven by Spikes.

[BibT_eX]

[DOI]

CoRR, 2024

Fair Graph Representation Learning via Sensitive Attribute Disentanglement.

[BibT_eX]

[DOI]

Proceedings of the ACM on Web Conference 2024, 2024

The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Rethinking and Simplifying Bootstrapped Graph Latents.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Topology-monitorable Contrastive Learning on Dynamic Graphs.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Spectral Adversarial Training for Robust Graph Neural Network.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., September, 2023

Adversarial Attack on Large Scale Graph.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2023

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs.

[BibT_eX]

[DOI]

CoRR, 2023

Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs.

[BibT_eX]

[DOI]

CoRR, 2023

What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

GUARD: Graph Universal Adversarial Defense.

[BibT_eX]

[DOI]

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Unifying multi-associations through hypergraph for bundle recommendation.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2022

Graph Enhanced Neural Interaction Model for recommendation.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2022

Are All Edges Necessary? A Unified Framework for Graph Purification.

[BibT_eX]

[DOI]

Zishan Gu

Jintang Li

Liang Chen

CoRR, 2022

MaskGAE: Masked Graph Modeling Meets Graph Autoencoders.

[BibT_eX]

[DOI]

CoRR, 2022

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.

[BibT_eX]

[DOI]

CoRR, 2022

GUARD: Graph Universal Adversarial Defense.

[BibT_eX]

[DOI]

CoRR, 2022

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift.

[BibT_eX]

[DOI]

CoRR, 2022

Neighboring Backdoor Attacks on Graph Convolutional Network.

[BibT_eX]

[DOI]

CoRR, 2022

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.

[BibT_eX]

[DOI]

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Spiking Graph Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

Phishing Scams Detection in Ethereum Transaction Network.

[BibT_eX]

[DOI]

ACM Trans. Internet Techn., 2021

Understanding Structural Vulnerability in Graph Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software.

[BibT_eX]

[DOI]

Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering: Companion Proceedings, 2021

2020

A Survey of Adversarial Learning on Graphs.

[BibT_eX]

[DOI]

CoRR, 2020

Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective.

[BibT_eX]

[DOI]

Proceedings of the Human Brain and Artificial Intelligence - Second International Workshop, 2020

Jintang Li

Timeline

Legend:

Links

Online presence:

On csauthors.net:

Bibliography

Loading...