Research on Dynamic Community Detection Method Based on Multi-dimensional Feature Information of Community Network

With the continuous development of technology, we have the ability to fully record all aspects of data information of every individual in the society, so how to utilize this information to create greater value is becoming more and more important. Compared with the traditional static community detection, the study of dynamic community detection is more in line with the real situation in the society. Thus, in this paper, a method that can utilize the information of diversified dynamic community networks is proposed, i.e., Dynamic Community Detection Method based on Multidimensional Feature Information of Community (Dcdmf), which utilizes neural networks with strong learning and adaptive capabilities, the ability to automatically extract useful features and process complex data, and the ability to process the graph nodes and the data between the nodes of the dynamic community network, and the ability to real-time adjust the current community representation data based on historical information, and record the current community representation data for the next moment of community data. The experimental results in the paper show that the method has a certain degree of effectiveness.