Graph Representation Learning
Graph representation learning aims to encode graph-structured data into low-dimensional vector representations suitable for machine learning tasks. Current research focuses on improving the expressiveness and efficiency of graph neural networks (GNNs), exploring alternative approaches like topological embeddings and leveraging large language models for enhanced interpretability and handling of text-attributed graphs. These advancements are crucial for tackling challenges in various domains, including recommendation systems, anomaly detection, and biological data analysis, where graph-structured data is prevalent and efficient, accurate analysis is critical.
Papers
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Gaichao Li, Jinsong Chen, John E. Hopcroft, Kun He
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu, Baokun Wang, Changhua Meng, Tengfei Liu, Tianyi Zhang, Ningtao Wang, Xing Fu, Qi Li, Weiqiang Wang
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
Jinsong Chen, Gaichao Li, John E. Hopcroft, Kun He