Influencer discovery algorithm in a multi-relational network

With the development of social networks, the interaction between users and the application of social platforms for communications has become increasingly diverse. The influence and authority of different users have also been distinguished in constant communications. To better research the dissemination mechanism of different users' views on social platforms, a multi-relational network model first had to be built that can retain the interactive relationship between social networks to the maximum extent. In this model, the node has an impact weight, while the linked edge has a link weight. Combining these features of a multi-relational network model, a discovery algorithm - the InfluencerRank algorithm - was proposed. This discovery algorithm accurately identifies the essential influential nodes in networks. By combining the data of cases with the InfluencerRank algorithm, we identified influencers and conducted a comparative analysis.