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Graph Prompt Learning: A Comprehensive Survey and Beyond
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Graph Prompt Learning: A Comprehensive Survey and Beyond
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Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively.
Forward citations
Cited by 11 Pith papers
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Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
GILT introduces a token-based in-context learning framework that unifies node, edge, and graph classification on heterogeneous graphs with numerical features, achieving tuning-free adaptation and stronger few-shot res...
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PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
PromptDyG performs unsupervised test-time prompt adaptation on frozen dynamic graph models via entropy minimization to guarantee larger positive-negative pair margins and improve online predictions.
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Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
ADPrompt adds per-node feature gating and layer-wise edge message calibration to frozen pre-trained GNNs, reducing attribute and structural bias while keeping node-classification accuracy competitive.
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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
GILT turns few-shot node, edge, and graph classification into a token-reasoning problem and reaches competitive accuracy on held-out benchmarks with no per-graph tuning and no LLM.
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PLACE: Prompt Learning for Attributed Community Search in Large Graphs
PLACE is a prompt-augmented graph framework for attributed community search that integrates learnable tokens with GNNs via alternating training and divide-and-conquer scaling, achieving 22% higher average F1 scores th...
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CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
CHoE introduces structure-conditioned experts with routing and semantic fusion to improve few-shot cross-domain heterogeneous graph prompt learning.
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CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
CHoE uses structure-conditioned experts, structure-aware routing with load balancing, and prompt-based semantic fusion to improve few-shot performance on cross-domain heterogeneous graph prompt learning tasks.
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CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Intermediate LLM thoughts are fed back to rewrite graph token embeddings each step, improving cross-dataset graph-LLM classification and link prediction.
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Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
LR-GMP unifies graph prompting via a low-rank Graph Message Prompt paradigm to achieve better generalization than component-specific methods.
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Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
GPH^2 pre-trains one expert per graph on edge-dropped or meta-path views and fuses frozen experts with class-wise attention, outperforming type-specific graph pre-training baselines.
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