  Data selection methods, such as active learning and core-set selection, are
useful tools for improving the data efficiency of deep learning models on
large-scale datasets. However, recent deep learning models have moved forward
from independent and identically distributed data to graph-structured data,
such as social networks, e-commerce user-item graphs, and knowledge graphs.
This evolution has led to the emergence of Graph Neural Networks (GNNs) that go
beyond the models existing data selection methods are designed for. Therefore,
we present Grain, an efficient framework that opens up a new perspective
through connecting data selection in GNNs with social influence maximization.
By exploiting the common patterns of GNNs, Grain introduces a novel feature
propagation concept, a diversified influence maximization objective with novel
influence and diversity functions, and a greedy algorithm with an approximation
guarantee into a unified framework. Empirical studies on public datasets
demonstrate that Grain significantly improves both the performance and
efficiency of data selection (including active learning and core-set selection)
for GNNs. To the best of our knowledge, this is the first attempt to bridge two
largely parallel threads of research, data selection, and social influence
maximization, in the setting of GNNs, paving new ways for improving data
efficiency.
