  In recent years, graph prompt learning/tuning has garnered increasing
attention in adapting pre-trained models for graph representation learning. As
a kind of universal graph prompt learning method, Graph Prompt Feature (GPF)
has achieved remarkable success in adapting pre-trained models for Graph Neural
Networks (GNNs). By fixing the parameters of a pre-trained GNN model, the aim
of GPF is to modify the input graph data by adding some (learnable) prompt
vectors into graph node features to better align with the downstream tasks on
the smaller dataset. However, existing GPFs generally suffer from two main
limitations. First, GPFs generally focus on node prompt learning which ignore
the prompting for graph edges. Second, existing GPFs generally conduct the
prompt learning on all nodes equally which fails to capture the importances of
different nodes and may perform sensitively w.r.t noisy nodes in aligning with
the downstream tasks. To address these issues, in this paper, we propose a new
unified Graph Selective Prompt Feature learning (GSPF) for GNN fine-tuning. The
proposed GSPF integrates the prompt learning on both graph node and edge
together, which thus provides a unified prompt model for the graph data.
Moreover, it conducts prompt learning selectively on nodes and edges by
concentrating on the important nodes and edges for prompting which thus make
our model be more reliable and compact. Experimental results on many benchmark
datasets demonstrate the effectiveness and advantages of the proposed GSPF
method.
