  Subjective Logic (SL) is one of well-known belief models that can explicitly
deal with uncertain opinions and infer unknown opinions based on a rich set of
operators of fusing multiple opinions. Due to high simplicity and
applicability, SL has been substantially applied in a variety of decision
making in the area of cybersecurity, opinion models, trust models, and/or
social network analysis. However, SL and its variants have exposed limitations
in predicting uncertain opinions in real-world dynamic network data mainly in
three-fold: (1) a lack of scalability to deal with a large-scale network; (2)
limited capability to handle heterogeneous topological and temporal
dependencies among node-level opinions; and (3) a high sensitivity with
conflicting evidence that may generate counterintuitive opinions derived from
the evidence. In this work, we proposed a novel deep learning (DL)-based
dynamic opinion inference model while node-level opinions are still formalized
based on SL meaning that an opinion has a dimension of uncertainty in addition
to belief and disbelief in a binomial opinion (i.e., agree or disagree). The
proposed DL-based dynamic opinion inference model overcomes the above three
limitations by integrating the following techniques: (1) state-of-the-art DL
techniques, such as the Graph Convolutional Network (GCN) and the Gated
Recurrent Units (GRU) for modeling the topological and temporal heterogeneous
dependency information of a given dynamic network; (2) modeling conflicting
opinions based on robust statistics; and (3) a highly scalable inference
algorithm to predict dynamic, uncertain opinions in a linear computation time.
We validated the outperformance of our proposed DL-based algorithm (i.e.,
GCN-GRU-opinion model) via extensive comparative performance analysis based on
four real-world datasets.
