  Graph Lottery Ticket (GLT), a combination of core subgraph and sparse
subnetwork, has been proposed to mitigate the computational cost of deep Graph
Neural Networks (GNNs) on large input graphs while preserving original
performance. However, the winning GLTs in exisiting studies are obtained by
applying iterative magnitude-based pruning (IMP) without re-evaluating and
re-considering the pruned information, which disregards the dynamic changes in
the significance of edges/weights during graph/model structure pruning, and
thus limits the appeal of the winning tickets. In this paper, we formulate a
conjecture, i.e., existing overlooked valuable information in the pruned graph
connections and model parameters which can be re-grouped into GLT to enhance
the final performance. Specifically, we propose an adversarial complementary
erasing (ACE) framework to explore the valuable information from the pruned
components, thereby developing a more powerful GLT, referred to as the ACE-GLT.
The main idea is to mine valuable information from pruned edges/weights after
each round of IMP, and employ the ACE technique to refine the GLT processing.
Finally, experimental results demonstrate that our ACE-GLT outperforms existing
methods for searching GLT in diverse tasks. Our code will be made publicly
available.
