  In recent years, online question-answering (Q&A) platforms, such as Stack
Exchange (SE), have become increasingly popular as a source of information and
knowledge sharing. Despite the vast amount of information available on these
platforms, many questions remain unresolved. In this work, we aim to address
this issue by proposing a novel approach to identify unresolved questions in SE
Q&A communities. Our approach utilises the graph structure of communication
formed around a question by users to model the communication network
surrounding it. We employ a property graph model and graph neural networks
(GNNs), which can effectively capture both the structure of communication and
the content of messages exchanged among users. By leveraging the power of graph
representation and GNNs, our approach can effectively identify unresolved
questions in SE communities. Experimental results on the complete historical
data from three distinct Q&A communities demonstrate the superiority of our
proposed approach over baseline methods that only consider the content of
questions. Finally, our work represents a first but important step towards
better understanding the factors that can affect questions becoming and
remaining unresolved in SE communities.
