Research Article
Community Discovery Algorithm Based on Parallel Recommendation in Cloud Computing
@INPROCEEDINGS{10.1007/978-3-030-67874-6_18, author={Jian-li Zhai and Fang Meng}, title={Community Discovery Algorithm Based on Parallel Recommendation in Cloud Computing}, proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2021}, month={1}, keywords={Social networks Recommendation system Overlapping communities}, doi={10.1007/978-3-030-67874-6_18} }- Jian-li Zhai
Fang Meng
Year: 2021
Community Discovery Algorithm Based on Parallel Recommendation in Cloud Computing
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-67874-6_18
Abstract
In the cloud computing environment, traditional social network community discovery algorithms have low accuracy in social network community discovery, leading to information waste, community overlap and low scalability, and unable to achieve ideal computing results. Therefore, a social network based on parallel recommendation is proposed. Network community discovery algorithm. By mining the candidate trusted user set, the number and composition of the community are obtained, and the communication units are divided into overlapping communities and non-overlapping communities according to the different numbers of communities belonging to the nodes in the network. Combining the mining of candidate trusted user sets and community division, social networking is realized Network community discovers and calculates. Experiments show that the algorithm improves the accuracy and stability of social network community discovery, and has good application value.
