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Found 11 papers, 6 papers with code
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
To address these issues, we propose MDGPT, a text free Multi-Domain Graph Pre-Training and adaptation framework designed to exploit multi-domain knowledge for graph learning.
DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs
For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique, which are generally pre-trained on the link prediction task, leaving a significant gap from the objectives of downstream tasks such as node classification.
Few-Shot Learning on Graphs: from Meta-learning to Pre-training and Prompting
Finally, we outline prospective future directions for few-shot learning on graphs to catalyze continued innovation in this field.
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design.
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge.
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs.
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
Fine-Grained Visual Recognition
Graph Representation Learning
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution
Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details.
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner.
Learning to Count Isomorphisms with Graph Neural Networks
However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting.
Count-GNN: Graph Neural Networks for Subgraph Isomorphism Counting
At the graph level, we modulate the graph representation conditioned on the query subgraph, so that the model can be adapted to each unique query for better matching with the input graph.
