• 🔭 I received my bachelor's and master's degrees from North China Electric Power University and Tianjin University respectively.

• 🔭 I'm currently working on the research related to the ERNIE Bot at Baidu.

• 🌱 My research includes LLM, RAG, Agent, Graph Machine Learning and Time Series Forecasting.

• 📫 How to reach me:lijunliang@tju.edu.cn.

• J. Li, Y. Yang, Q. Hu, X. Wang, and H. Gao, Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion (NeurIPS 2023)
• J. Li, Y. Yang, Y. Zhang, Q. Hu, A. Zhao, and H. Gao, Public Opinion Field Effect and Hawkes Process Join Hands for Information Popularity Prediction (AAAI 2025)
• J. Li, Y. Yang, Q. Hu, X. Wang, and H. Gao, Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks (ICDM 2023)
• J. Li, Y. Yang, Q. Hu, X. Wang, and H. Gao, Hierarchical Label Inference in Dynamic Attributed Networks via Label Semantics (Under Peer Review (TOIS 2024))
• J. Li, R. Jiao, S. Wang, and H. He, An Ensemble Load Forecasting Model Based on Online Error Updating (Proceedings of the CSEE)
• R. Jiao, J. Chu,J. Li, and W. Zhang, Personalized Federated Multi-region Load Forecasting Method Based on Data Decomposition (Proceedings of the CSEE)

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1. pyg-team/pytorch_geometricpyg-team/pytorch_geometricPublic

   Graph Neural Network Library for PyTorch

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2. GNNs-for-Node-ClassificationGNNs-for-Node-ClassificationPublic

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3. GNNs-for-Link-PredictionGNNs-for-Link-PredictionPublic

   Some GNNs are implemented using PyG for link prediction tasks, including: GCN, GraphSAGE, GAT, Node2Vec、RGCN、HGT and HAN, which will continue to be updated in the future.

   Python 57 3

4. FedProx-PyTorchFedProx-PyTorchPublic

   PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).

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5. Scaffold-Federated-LearningScaffold-Federated-LearningPublic

   PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

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6. FedPerFedPerPublic

   PyTorch implementation of FedPer (Federated Learning with Personalization Layers).

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