  Nowadays, social media plays an important role in many fields, such as the
promotion of measures against major infectious diseases, merchandising, etc. In
social media, some people are known as opinion leaders due to their strong
ability to influence the opinions of others. The detection of opinion leaders
has become an important task in social network analysis. Social networks are
often represented in the form of graphs which allows a large number of graph
analysis methods to be used for opinion leader detection. Some studies have
attempted to apply graph representation learning for opinion leader detection
and achieved good results. In this paper, we propose a model-agnostic framework
that formulate the opinion leader detection problem as a ranking task of node
embeddings. A variety of methods and datasets are chosen to analyze the
performance of our framework both qualitatively and quantitatively. Based on
the analysis results, we propose a strategy that combines opinion leaders
detected by two different ranking algorithms to obtain a more comprehensive set
of opinion leaders. And we analyze the temporal changes of the opinion leaders
in one of the dynamic social networks.
