104 citations · 168 across the 4 of their papers we have counts for
4 papers
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Semi-Supervised Domain Generalization with Evolving Intermediate Domain
Luojun Lin, Han Xie, Zhishu Sun +4
Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which ca…
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong +1
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can al…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…