  Recently, graph-based algorithms have drawn much attention because of their
impressive success in semi-supervised setups. For better model performance,
previous studies learn to transform the topology of the input graph. However,
these works only focus on optimizing the original nodes and edges, leaving the
direction of augmenting existing data unexplored. In this paper, by simulating
the generation process of graph signals, we propose a novel heuristic
pre-processing technique, namely ELectoral COllege (ELCO), which automatically
expands new nodes and edges to refine the label similarity within a dense
subgraph. Substantially enlarging the original training set with high-quality
generated labeled data, our framework can effectively benefit downstream
models. To justify the generality and practicality of ELCO, we couple it with
the popular Graph Convolution Network and Graph Attention Network to perform
extensive evaluations on three standard datasets. In all setups tested, our
method boosts the average score of base models by a large margin of 4.7 points,
as well as consistently outperforms the state-of-the-art. We release our code
and data on https://github.com/RingBDStack/ELCO to guarantee reproducibility.
