  The rapid advances in automation technologies, such as artificial
intelligence (AI) and robotics, pose an increasing risk of automation for
occupations, with a likely significant impact on the labour market. Recent
social-economic studies suggest that nearly 50\% of occupations are at high
risk of being automated in the next decade. However, the lack of granular data
and empirically informed models have limited the accuracy of these studies and
made it challenging to predict which jobs will be automated. In this paper, we
study the automation risk of occupations by performing a classification task
between automated and non-automated occupations. The available information is
910 occupations' task statements, skills and interactions categorised by
Standard Occupational Classification (SOC). To fully utilize this information,
we propose a graph-based semi-supervised classification method named
\textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) to
identify the automated risk for occupations. This model integrates a
heterogeneous graph to capture occupations' local and global contexts. The
results show that our proposed method outperforms the baseline models by
considering the information of both internal features of occupations and their
external interactions. This study could help policymakers identify potential
automated occupations and support individuals' decision-making before entering
the job market.
