  Message passing is the core of most graph models such as Graph Convolutional
Network (GCN) and Label Propagation (LP), which usually require a large number
of clean labeled data to smooth out the neighborhood over the graph. However,
the labeling process can be tedious, costly, and error-prone in practice. In
this paper, we propose to unify active learning (AL) and message passing
towards minimizing labeling costs, e.g., making use of few and unreliable
labels that can be obtained cheaply. We make two contributions towards that
end. First, we open up a perspective by drawing a connection between AL
enforcing message passing and social influence maximization, ensuring that the
selected samples effectively improve the model performance. Second, we propose
an extension to the influence model that incorporates an explicit quality
factor to model label noise. In this way, we derive a fundamentally new AL
selection criterion for GCN and LP--reliable influence maximization (RIM)--by
considering quantity and quality of influence simultaneously. Empirical studies
on public datasets show that RIM significantly outperforms current AL methods
in terms of accuracy and efficiency.
