I am Jiong (煚), a Ph.D. student in Computer Science and Engineering at the University of Michigan working with Danai Koutra. My research interest is on Graph Neural Network (GNN). Currently, I am working on understanding and improving GNNs on graphs with complex properties, such as graphs which go beyond the traditional homophily assumption (i.e., where the linked nodes are similar) by showing heterophily.
I was a Master’s student in Electrical and Computer Engineering at UM. Before joining UM, I received my bachelor degree at Xi’an Jiaotong University, where I was a student of the Special Class for the Gifted Young.
Apart from my study, I am a savvy tech user and a go-to friend for coding and tech questions.
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Advances in Neural Information Processing Systems 33 (2020).
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Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sripada, and Danai Koutra. 2019. GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19). ACM, New York, NY, USA, 772-782.
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Jiong Zhu, Ryan Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra. Graph Neural Networks with Heterophily.
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