45 citations · 82 across the 3 of their papers we have counts for
5 papers
Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
Hanxu Yang, Yuhuan Zhao, Xiaodong He +1
Pre-training Graph Neural Networks (GNNs) via self-supervised learning has become a dominant paradigm, yet efficiently adapting frozen encoders remains a challenge. Graph prompting…
SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models
Xiaodong He, Xincheng Wang, Zhao Kang
Graph foundation models (GFMs) aim to learn transferable representations across severely heterogeneous graph domains. However, severe domain shifts in topology, graph scale, and fe…
Structure-Centric Graph Foundation Model via Geometric Bases
Xiaodong He, Haolan He, Ruiyi Fang +2
Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible node feature spaces. We propose S…
PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
Zhewei Wei, Xiaodong He, Xiaokui Xiao +4
{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node in graph , a {\em single-source SimRank query} returns the SimRank similarit…
ProbeSim: Scalable Single-Source and Top-k SimRank Computations on Dynamic Graphs
Yu Liu, Bolong Zheng, Xiaodong He +4
Single-source and top- SimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection,…