3 citations · 3 across the 16 of their papers we have counts for
15 papers · 1 filter
More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training
Yupeng Tong, Lianze Shan, Jitao Zhao +2
Inspired by the great success of foundation models in the development of artificial general intelligence, Graph Foundation Models (GFMs) have recently attracted significant attenti…
DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion
Peiyuan Li, Yongqi Huang, Jitao Zhao +2
Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language understanding capabilities. Combin…
Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models
Yi Wang, Jitao Zhao, Di Jin +1
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains. Unlike traditional graph learning methods that…
What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models
Dongxiao He, Siqi Liu, Jitao Zhao +3
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for general-purpose graph learning, aiming to learn reusable knowledge that generalizes across diverse…
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
Jingbo Cui, Jitao Zhao, Di Jin +1
Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs…
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
Lianze Shan, Ningchong Wang, Jitao Zhao +2
Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream…
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
Peiyuan Li, Yongqi Huang, Jitao Zhao +3
Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream app…
Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Dongxiao He, Ruqiong Zhang, Zhizhi Yu +4
Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network…
SPR: Toward a Graph Foundation Model for Transferable Graph Cognition via Spectral Patterns and Relational Geometry
Ankang Yang, Jitao Zhao, Dongxiao He +3
Recently, Graph Foundation Models (GFMs) have attracted increasing attention for their potential to learn unified and generalizable knowledge across diverse graphs, thereby support…
LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training
Lianze Shan, Jitao Zhao, Dongxiao He +3
Recent advances in generic large models, such as GPT and DeepSeek, have motivated the introduction of universality to graph pre-training, aiming to learn rich and generalizable kno…
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Lianze Shan, Jitao Zhao, Dongxiao He +3
Universal graph pre-training has emerged as a key paradigm in graph representation learning, offering a promising way to train encoders to learn transferable representations from u…
Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
Chundong Liang, Yongqi Huang, Dongxiao He +4
Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most existing methods are designed f…
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
Dongxiao He, Wenxuan Sun, Yongqi Huang +2
Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misalignment between pre-training obj…
Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
Dongxiao He, Yongqi Huang, Jitao Zhao +2
Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations…
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Yucheng Jin, Yun Xiong, Juncheng Fang +5
Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emer…