Edge intelligence-enabled dynamic overlapping community discovery and evolution prediction in social media data streams

Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber-physical-social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper-connected smart sensing devices. As a part of future CPSS, online social networks are large-scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence-enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state-of-the-art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.