9 citations · 14 across the 6 of their papers we have counts for
6 papers
UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
Taoran Fang, Yan Deng, Chunping Wang +3
With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such het…
Handling Feature Heterogeneity with Learnable Graph Patches
Yifei Sun, Yang Yang, Xiao Feng +4
In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model…
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
Linfeng Cao, Haoran Deng, Yang Yang +2
Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-scale web graphs in real applications poses sign…
Enhancing Cross-domain Link Prediction via Evolution Process Modeling
Xuanwen Huang, Wei Chow, Yize Zhu +5
This work proposes DyExpert, a dynamic graph model for cross-domain link prediction. It can explicitly model historical evolving processes to learn the evolution pattern of a speci…
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…