Manuscripts in progress

  1. T.T. He, H. Zhou, Y.S. Ong, and G. Cong, “Graph selective attention networks for semi-supervised learning,” 2023. (PDF)


    Journal Papers

  2. T.T. He, Y. Liu, Y.S. Ong, X. Wu, and X. Luo, “Polarized message-passing in graph neural networks,” Artificial Intelligence, vol. 331, p. 104129, 2024. (PDF) (Pytorch Implementation)
  3. H. Zhou, T.T. He, Y.S. Ong, G. Cong, and Q. Chen, “Differentiable clustering for graph attention,” IEEE Transactions on Knowledge and Data Engineering, 2024 (Accepted). (Pytorch implementation)
  4. F. Bi, T.T. He, and X. Luo, “A fast nonnegative autoencoder based approach to latent factor analysis on high dimensional and incomplete data,” IEEE Transactions on Services Computing, 2023.
  5. L. Yao, Y. Zhang, T.T. He, and H. Luo, “Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction,” Applied Energy, vol. 352, pp. 121975, 2023.
  6. L. Yao, T.T. He, and H. Luo, “Piggybacking on past problem for faster optimization in aluminum electrolysis process design,” Engineering Applications of Artificial Intelligence, vol. 126, pp. 106937, 2023.
  7. F. Bi, T.T. He, Y. Xie, and X. Luo, “Two-stream graph convolutional network-incorporated latent feature analysis,” IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 3027-3042, 2023.
  8. Y. Xie, Y. Liang, M. Gong, Y.S. Ong, K. Qin, and T.T. He, “Semi-supervised graph neural networks for graph classification,” IEEE Transactions on Cybernetics, vol. 53, no. 10, pp. 6222-6235, 2023.
  9. Z. Guo, Y.S. Ong, T.T. He, and H. Liu, “Co-learning Bayesian optimization,” IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9820-9833, 2022.
  10. L. Yao, W. Ding, T.T. He, S. Liu, and L. Nie, “A multiobjective prediction model with incremental learning ability by developing a multi-source filter neural network for the electrolytic aluminium process, Applied Intelligence, 2022. (PDF)
  11. T.T. He, Y.S. Ong, and P. Hu, “Multi-source propagation aware network clustering,” Neurocomputing, vol. 453, pp. 119-130, 2021. (PDF) (Matlab Implementation)
  12. T.T. He, L. Bai, and Y.S. Ong, “Vicinal vertex allocation for matrix factorization in networks,” IEEE Transactions on Cybernetics, 2021. (PDF) (Matlab Implementation)
  13. W. Ding, L. Yao, Y. Li, W. Long, J. Yi, and T.T. He, “Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system,” Applied Soft Computing, vol. 99, pp. 106925, 2021.
  14. L. Hu, H. Yan, P. Hu, and T.T. He, “Exploiting higher-order patterns for community detection in attributed graphs,” Integrated Computer-Aided Engineering, vol. 28, no. 2, pp. 207-218, 2021.
  15. L. Bai, Y.S. Ong, T.T. He, and A. Gupta, “Multi-task gradient descent for multi-task learning,” Memetic Computing, vol. 12, pp. 355-369, 2020. (PDF)
  16. P. Hu, Z. Kuang, T.T. He, Y. Huang, Z. Tang, S. Li, and J. Mei, “2286-PUB: profiling microRNA (miRNA) and metabolic disease association by using diffusion model,” Diabetes, vol. 69, supp. 1, 2020.
  17. T.T. He, Y. Liu, T.H. Ko, K.C.C. Chan, and Y.S. Ong, "Contextual correlation preserving multi-view featured graph clustering," IEEE Transactions on Cybernetics, vol. 50, no. 10, pp. 4318 - 4331, 2020. (PDF) (Matlab Implementation)
  18. X. Han, T.T. He, Y.S. Ong, and Y. Zhong, “Precise object detection using adversarially augmented local/global feature fusion,” Engineering Applications of Artificial Intelligence, vol. 94, pp. 103710, 2020. (PDF)
  19. T.T. He, and K.C.C. Chan, "Measuring boundedness for protein complex identification in PPI networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 3, pp. 967-979, 2019. (PDF)
  20. T.T. He, and K.C.C. Chan, “Discovering fuzzy structural patterns for graph analytics,” IEEE Transactions on Fuzzy Systems, vol. 26, no.5, pp. 2785-2796, 2018. (PDF) (Matlab Executable)
  21. T.T. He, L. Hu, K.C.C. Chan, and P. Hu, “Learning latent factors for community identification and summarization,” IEEE Access, vol. 6, pp. 30137 - 30148, 2018. (PDF) (Matlab Executable)
  22. T.T. He, and K.C.C. Chan, “MISAGA: an algorithm for mining interesting subgraphs in attributed graphs,” IEEE Transactions on Cybernetics, vol. 48, no. 5, pp. 1369-1382, 2018. (PDF) (Matlab Executable)
  23. T.T. He, and K.C.C. Chan, “Evolutionary graph clustering for protein complex identification,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 3, pp. 892-904, 2018. (PDF) (Java Executable)
  24. Y. Hu, J.J. You, J.N.K. Liu, and T.T. He, “An eigenvector based center selection for fast training scheme of RBFNN,” Information Sciences, vol. 428, pp. 62-75, 2018.
  25. K.C.C. Chan, and T.T. He, "Data science: the demand and development of talents," Big Data Research, vol. 2, no. 5, pp. 2016058, 2016. (PDF)


    Conference Papers

  26. Z. Li, X. Wu, X. Tang, T.T. He, Y.S. Ong, M. Chen, Q. Liu, Q. Lao, X. Li, H. Yu, “Benchmarking data heterogeneity evaluation approaches for personalized federated learning,” FL@FM-NeurIPS, 2024.
  27. H. Zhou, W. Huang, Y. Chen, T.T. He, G. Cong, and Y.S. Ong, “Road network representation learning with the Third Law of Geography,” Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024), 2024. (PDF)
  28. M. Chen, X. Wu, X. Tang, T.T. He, Y.S. Ong, Q. Liu, Q. Lao, and H. Yu, “Free-rider and conflict aware collaboration formation for cross-silo federated learning,” Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024), 2024.
  29. S. Tan, H. Cheng, X. Wu, H. Yu, T.T. He, Y.S. Ong, C. Wang, and X. Tao, “FedCompetitors: harmonious collaboration in federated learning with competing participants,” 38th AAAI Conference on Artificial Intelligence (AAAI-24), 2024. (PDF)
  30. F. Bi, T.T. He, and X. Luo, “A two-stream light graph convolution network-based latent factor model for accurate cloud service QoS estimation,” 22nd IEEE International Conference on Data Mining (ICDM 2022), 2022.
  31. T.T. He, Y.S. Ong, and L. Bai, “Learning conjoint attentions for graph neural nets,” Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), 2021. (PDF) (PyTorch Implementation)
  32. L. Yao, and T.T. He, “Fuzzy community detection with multi-view correlated topics,” Twentieth IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2021. (PDF)
  33. P. Hu, T.T. He, Z. Niu, S. Li, B. Hao, and J. Mei, "Towards interpretable adverse drug reaction prediction using deep graph fusion," AAAI 2020 Workshop, 2020.
  34. T.T. He, L. Bai, and Y.S. Ong, "Manifold regularized stochastic block model," IEEE International Conference on Tools with Artificial Intelligence (ICTAI'19), 2019. (ICTAI'19 Best Paper Award) (PDF)
  35. L. Hu, P. Hu, and T.T. He, "Exploiting higher-order patterns for community detection in attributed graphs," International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019), 2019.
  36. P. Hu, Z. You, T.T. He, S Li, S Gu, and K.C.C. Chan, "Learning latent patterns in molecular data for explainable drug side effects prediction," in Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM'18), 2018, pp. 1163-1169.
  37. T.H. Ko, Z. Gu, T.T. He, and Y. Liu "Towards Learning Emotional Subspace," MediaEval 2018. (PDF)
  38. P. Hu, Z. Niu, T.T. He, K.C.C Chan, "Learning deep representations in large integrated networks for graph clustering," IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE'18), 2018, pp. 101-105.
  39. T.T. He, K.C.C. Chan, and L. Yang, "Clustering in networks with multi-modality attributes," in Proc. IEEE/WIC/ACM International Conference on Web Intelligence (WI’18), 2018, pp. 401-406. (PDF)
  40. T.T. He, and K.C.C. Chan, “Learning latent factors in linked multi-modality data,” in Proc. International Symposium on Methodologies for Intelligent Systems (ISMIS’18), 2018, pp. 214-224.
  41. P. Hu, T.T. He, K.C.C. Chan, and H. Leung, “Deep fusion of multiple networks for learning latent social communities,” in Proc. IEEE International Conference on Tools with Artificial Intelligence (ICTAI’17), 2017, pp. 765-771.
  42. P. Hu, K.C.C. Chan, and T.T. He, “Deep graph clustering in social network,” in Proc. ACM International Conference on World Wide Web (WWW'17), 2017, pp. 1425-1426.
  43. T.T. He, and K.C.C. Chan, "Evolutionary community detection in social networks," in Proc. 2014 IEEE Congress on Evolutionary Computation (CEC'14), 2014, pp. 1496-1503. (PDF)