5 papers · 2 filters
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
Zhengyu Wu, Daohan Su, Yang Zhang +3
Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information. However, existing methods…
Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
Xunkai Li, Daohan Su, Sicheng Liu +5
Inspired by the success of LLMs, GFMs are designed to learn the optimal embedding functions from multi-domain text-attributed graphs for the downstream cross-task generalization ca…
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Zhengyu Wu, Boyang Pang, Xunkai Li +6
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the…
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
Xunkai Li, Daohan Su, Zhengyu Wu +4
The -parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node featu…
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
Zhengyu Wu, Guang Zeng, Huilin Lai +7
Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preservi…