Community overlap discovery algorithm based on industrial big data
Industrial big data has a large scale,complex structure,and high value density.To deeply explore and ana-lyze its hidden relationships,trends and patterns,and to provide better decision-making basis for enterprises,com-bined with the idea of random walk and label propagation,a community overlap discovery algorithm based on indus-trial big data was proposed.The algorithm of seed node selection was designed,the importance of each node was cal-culated by random walk,and the irrelevant and important seed nodes were selected.Then,an overlapping communi-ty discovery algorithm was proposed,the seed node was given a unique label,and the label was propagated iterative-ly until the node label was no longer changed.The final overlapping community division result was obtained accord-ing to the node label.Finally,comparative experiments were carried out on real data sets and artificial data sets,the results showed that the algorithm could effectively find high-quality overlapping communities on the network.The algorithm could be applied to data analysis and information mining of industrial big data.
industrial big datacommunity detectionoverlapping communityrandom walklabel propagation
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