The academic journal published by IEEE(Institute of Electrical and Electronics Engineers) proposed a new model to improve the quality of community discovery. This new proposed model addresses some of the limitations that traditional methods had and produces result with higher correspondence with the ground truth communities.
Traditionally, community discovery methods treat network structure as a static topology. They neglect the interaction between the information factor, as they only represent the possibility of interaction between users. However modern microblogging networks applications like Twitter, are extremly dynamic in content distribution and topological structure. The flow of information that is not considered in traditional methods could be applied to microblogging networks to better determine user interest community.
The proposed method uses multiple different data analysis algorithms to filter and integrate information interactions between users. It then uses machine learning strategy to analyze information to dynamically updates the characteristic of the network to cluster the community.
This is interesting to me because, as we have learnt in class, sometimes data are lost in translation. This method takes some of the missing information into account and analyze them before translation. The resulting communities have higher correspondence and accuracy, which can provide more information than traditional methods.
Reference:
Jiang, Liang, et al. “An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People.” IEEE Internet of Things Journal, 2019, pp. 1–1., doi:10.1109/jiot.2019.2893625.