Jintang Li

Orcid: 0000-0002-6405-1531

According to our database1, Jintang Li authored at least 36 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective.
CoRR, 2024

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes.
CoRR, 2024

State Space Models on Temporal Graphs: A First-Principles Study.
CoRR, 2024

SGHormer: An Energy-Saving Graph Transformer Driven by Spikes.
CoRR, 2024

Fair Graph Representation Learning via Sensitive Attribute Disentanglement.
Proceedings of the ACM on Web Conference 2024, 2024

The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation.
Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Rethinking and Simplifying Bootstrapped Graph Latents.
Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

2023
Spectral Adversarial Training for Robust Graph Neural Network.
IEEE Trans. Knowl. Data Eng., September, 2023

Adversarial Attack on Large Scale Graph.
IEEE Trans. Knowl. Data Eng., 2023

The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation.
CoRR, 2023

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning.
CoRR, 2023

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs.
CoRR, 2023

Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning.
CoRR, 2023

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks.
CoRR, 2023

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs.
CoRR, 2023

What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding.
Proceedings of the IEEE International Conference on Data Mining, 2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

GUARD: Graph Universal Adversarial Defense.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Unifying multi-associations through hypergraph for bundle recommendation.
Knowl. Based Syst., 2022

Graph Enhanced Neural Interaction Model for recommendation.
Knowl. Based Syst., 2022

Are All Edges Necessary? A Unified Framework for Graph Purification.
CoRR, 2022

MaskGAE: Masked Graph Modeling Meets Graph Autoencoders.
CoRR, 2022

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
CoRR, 2022

GUARD: Graph Universal Adversarial Defense.
CoRR, 2022

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift.
CoRR, 2022

Neighboring Backdoor Attacks on Graph Convolutional Network.
CoRR, 2022

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
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.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021
Phishing Scams Detection in Ethereum Transaction Network.
ACM Trans. Internet Techn., 2021

Understanding Structural Vulnerability in Graph Convolutional Networks.
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.
Proceedings of the 43rd IEEE/ACM International Conference on Software Engineering: Companion Proceedings, 2021

2020
A Survey of Adversarial Learning on Graphs.
CoRR, 2020

Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective.
Proceedings of the Human Brain and Artificial Intelligence - Second International Workshop, 2020


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