Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Bo Jiang, Hao Wu, Beibei Wang, Jin Tang, and Bin Luo

TL;DR
This paper introduces Graph Sparse Prompting (GSP), a method that adaptively selects key graph elements for efficient and compact fine-tuning of pre-trained GNNs, improving performance on multiple benchmarks.
Contribution
It proposes a novel sparse prompting framework for GNNs, enabling attribute selection and compact tuning, which is more efficient than existing all-element prompting methods.
Findings
Effective on 16 benchmark datasets
Achieves more compact prompts with maintained or improved accuracy
Demonstrates advantages over existing prompting methods
Abstract
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes, etc.), which is suboptimal and obviously redundant. To address this issue, we propose exploiting sparse representation theory for graph prompting and present Graph Sparse Prompting (GSP). GSP aims to adaptively and sparsely select the optimal elements (e.g., certain node attributes) to achieve compact prompting for downstream tasks. Specifically, we propose two kinds of GSP models, termed Graph Sparse Feature Prompting (GSFP) and Graph Sparse multi-Feature Prompting (GSmFP). Both GSFP and GSmFP provide a general scheme for tuning any specific pre-trained GNNs that can achieve attribute selection and compact prompt learning simultaneously.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Bioinformatics and Genomic Networks
MethodsSoftmax · Attention Is All You Need
