This algorithm is applied in a dynamic environment to partition a transportation network into connected homogeneous regions that evolve with time. In this paper, we propose an efficient evolutionary spectral clustering algorithm that solves the drawbacks of evolutionary spectral clustering by reducing the size of the eigenvalue problem. However, the disadvantages of this algorithm are the cubic time complexity and the high memory demand, which make it insufficient to handle a large number of data sets. Evolutionary spectral clustering represents a state-of-the-art algorithm for grouping objects evolving over time. However, this type of classification is not suitable for data that change over time. Spectral clustering has been successfully applied for the partitioning of transportation networks based on the spatial characteristics of congestion at a specific time. Partitioning a transport network into homogeneous areas can be very useful for monitoring traffic as congestion is spatially correlated in adjacent roads, and it propagates at different speeds as a function of time. Road traffic congestion has became a major problem in most countries because it affects sustainable mobility. This method can also be applied to the general social networking community discovery field. It is proven that the proposed PPPM method is robust, reasonable, and effective. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and PisCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real networks. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out.
Then, the quadratic form of the error function is minimized. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently.
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