  With the advances of IoT developments, copious sensor data are communicated
through wireless networks and create the opportunity of building Digital Twins
to mirror and simulate the complex physical world. Digital Twin has long been
believed to rely heavily on domain knowledge, but we argue that this leads to a
high barrier of entry and slow development due to the scarcity and cost of
human experts. In this paper, we propose Digital Twin Graph (DTG), a general
data structure associated with a processing framework that constructs digital
twins in a fully automated and domain-agnostic manner. This work represents the
first effort that takes a completely data-driven and (unconventional) graph
learning approach to addresses key digital twin challenges.
