  We present a module that extends the temporal graph of a graph convolutional
network (GCN) for action recognition with a sequence of skeletons. Existing
methods attempt to represent a more appropriate spatial graph on an
intra-frame, but disregard optimization of the temporal graph on the
interframe. Concretely, these methods connect between vertices corresponding
only to the same joint on the inter-frame. In this work, we focus on adding
connections to neighboring multiple vertices on the inter-frame and extracting
additional features based on the extended temporal graph. Our module is a
simple yet effective method to extract correlated features of multiple joints
in human movement. Moreover, our module aids in further performance
improvements, along with other GCN methods that optimize only the spatial
graph. We conduct extensive experiments on two large datasets, NTU RGB+D and
Kinetics-Skeleton, and demonstrate that our module is effective for several
existing models and our final model achieves state-of-the-art performance.
