  Despite the success of the carefully-annotated benchmarks, the effectiveness
of existing graph neural networks (GNNs) can be considerably impaired in
practice when the real-world graph data is noisily labeled. Previous
explorations in sample selection have been demonstrated as an effective way for
robust learning with noisy labels, however, the conventional studies focus on
i.i.d data, and when moving to non-iid graph data and GNNs, two notable
challenges remain: (1) nodes located near topological class boundaries are very
informative for classification but cannot be successfully distinguished by the
heuristic sample selection. (2) there is no available measure that considers
the graph topological information to promote sample selection in a graph. To
address this dilemma, we propose a $\textit{Topological Sample Selection}$
(TSS) method that boosts the informative sample selection process in a graph by
utilising topological information. We theoretically prove that our procedure
minimizes an upper bound of the expected risk under target clean distribution,
and experimentally show the superiority of our method compared with
state-of-the-art baselines.
