Yuankai Luo (罗元凯)

Email: yuankailuo@nju.edu.cn

[Google Scholar] [Github]

Now I am an Assistant Professor at the School of Artificial Intelligence, Nanjing University (NJU). I received my Ph.D. degree from the School of Computer Science and Engineering at Beihang University, where I was supervised by Prof. Lei Shi and jointly trained at The Hong Kong Polytechnic University under the supervision of Prof. Xiao-Ming Wu. Before that, I did research supervised by Veronika Thost.

My research interests span Graph Neural Network (GNN) research, including architecture design, pre-training strategies, and compression/acceleration:

GNN Architecture and Analysis:

• Unified Framework GNN+ for Reassessing Classic GNNs in General Graph Tasks: developed GNN+, a framework that integrates message passing and well-known regularization techniques like dropout. GNN+ demonstrates that the true potential of classic GNNs has been previously underestimated in both node-level and graph-level tasks, challenging the belief that complex mechanisms are necessary for superior performance in graph models [NeurIPS 2024, ICML 2025, ICLR 2025].
• Graph Transformers for Specialized Small-Scale Graph Tasks: designed specialized Graph Transformers architecture tailored for small-scale graph tasks, particularly directed acyclic graphs [NeurIPS 2023, KDD 2023], and graphs with multi-level structures [NeurIPS 2024].

GNN Pre-training: proposed a graph self-supervised learning framework based on persistent homology theory, which effectively captures the multi-scale topological features of graph data [NeurIPS 2023].

GNN Compression: introduced vector quantization to compress continuous node embeddings into highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations—termed Node IDs [ICLR 2025].