64 citations · 122 across the 16 of their papers we have counts for
5 papers · 1 filter
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
Yifei Sun, Qi Zhu, Yang Yang +4
Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generall…
Towards Fair Graph Federated Learning via Incentive Mechanisms
Chenglu Pan, Jiarong Xu, Yue Yu +5
Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current ef…
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Jiarong Xu, Renhong Huang, Xin Jiang +4
Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for downstream tasks with unlabeled data, and it has recently become an active research area. The…
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Haoran Deng, Yang Yang, Jiahe Li +3
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changi…
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
Yuxuan Cao, Jiarong Xu, Carl Yang +5
In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Desp…