  In this paper, we explore a novel model reusing task tailored for graph
neural networks (GNNs), termed as "deep graph reprogramming". We strive to
reprogram a pre-trained GNN, without amending raw node features nor model
parameters, to handle a bunch of cross-level downstream tasks in various
domains. To this end, we propose an innovative Data Reprogramming paradigm
alongside a Model Reprogramming paradigm. The former one aims to address the
challenge of diversified graph feature dimensions for various tasks on the
input side, while the latter alleviates the dilemma of fixed per-task-per-model
behavior on the model side. For data reprogramming, we specifically devise an
elaborated Meta-FeatPadding method to deal with heterogeneous input dimensions,
and also develop a transductive Edge-Slimming as well as an inductive
Meta-GraPadding approach for diverse homogenous samples. Meanwhile, for model
reprogramming, we propose a novel task-adaptive Reprogrammable-Aggregator, to
endow the frozen model with larger expressive capacities in handling
cross-domain tasks. Experiments on fourteen datasets across node/graph
classification/regression, 3D object recognition, and distributed action
recognition, demonstrate that the proposed methods yield gratifying results, on
par with those by re-training from scratch.
