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portfolio
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publications
Self-Supervised Attributed Graph Learning: A Comprehensive Review
Published in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
A review of self-supervised learning methods on attributed graphs.
Recommended citation: Xie, Y., Xu, Z., Ji, J., Wang, Z., Wang, S., Liu, J., Ding, T., Hou, Z., and Tang, J. (2021). "Self-Supervised Attributed Graph Learning: A Comprehensive Review." IEEE TKDE.
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GraphMAE: Self-Supervised Masked Graph Autoencoders
Published in International Conference on Machine Learning (ICML), 2022
ICML 2022 paper proposing masked autoencoding for self-supervised graph representation learning.
Recommended citation: Hou, Z., Liu, X., Cen, Y., Dong, Y., Jiang, H., Yang, H., Wang, C., and Tang, J. (2022). "GraphMAE: Self-Supervised Masked Graph Autoencoders." ICML.
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GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Published in ACM Web Conference (WWW), 2023
WWW 2023 paper extending masked graph autoencoding with enhanced decoding for stronger self-supervised graph representation learning.
Recommended citation: Hou, Z., et al. (2023). "GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner." WWW.
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Harnessing Large Language Models for Hyperedge Prediction
Published in AAAI Conference on Artificial Intelligence (AAAI), 2024
Using LLMs and hypergraph learning for hyperedge prediction tasks.
Recommended citation: Hou, Z., Fang, Y., Liu, Z., Cen, Y., Zheng, V., and Tang, J. (2024). "Harnessing Large Language Models for Hyperedge Prediction." AAAI.
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ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Published in arXiv, 2024
A technical report of the ChatGLM model family from GLM-130B to GLM-4.
Recommended citation: Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Hou, Z., and others. (2024). "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools." arXiv:2406.12793.
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T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Published in International Conference on Machine Learning (ICML), 2025
T1 studies reasoning improvements via reinforcement learning and inference-time scaling.
Recommended citation: Hu, W., Xu, C., Hou, Z., et al. (2025). "T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling." ICML.
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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Published in arXiv, 2025
GLM-4.5 introduces ARC-oriented large language models for agentic behavior, reasoning, and coding.
Recommended citation: Zhipu AI et al. (2025). "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models." arXiv:2508.06471.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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