17 papers
Federated Graph Retrieval-Augmented Generation
Xiaobao Wang, Renda Han, Zhiliang Xia +3
Graph Retrieval-Augmented Generation (GraphRAG) leverages centralized knowledge graphs to enhance large language models with structured knowledge retrieval. However, real-world kno…
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…
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
Dongxiao He, Jiayu Zhang, Jitao Zhao +2
Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node…
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…
CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer
Ankang Yang, Jitao Zhao, Di Jin +2
Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks. Real-world graphs associate nodes with text, images,…
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…
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…
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He +5
Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment bet…
A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Di Jin, Jun Yang, Xiaobao Wang +3
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and ins…
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
Yongqi Huang, Jitao Zhao, Dongxiao He +3
Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining posi…