The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods.

随着时间的推移，网络结构呈现出各种变化。将这种结构与动态网络中社区的发展相融合，对于分析整个网络的演化和发展具有重要作用。如何保证社交网络大数据中社区结构的划分，以及保证当前时间段与前一时间段之间社区的连续性，是需要探索的问题。该问题可以通过融合动态社交网络社区的时间可变性、稳定性和连续性三个特征，并采用多目标优化方法检测动态网络中的社区结构来解决。在算法的初始步骤中加入概率融合方法，生成合适的网络分区，保证收敛速度快，精度高。提出了两种适用于社区的邻近融合策略：邻居多样性策略和邻居人群策略。这两种策略对候选网络分区进行了不同的更改。制定了动态社区演化的连续性度量，以比较两个连续时间步长的动态网络社区的相似性。在合成数据集和实际数据集上的实验证明，本文提出的方法比现有方法具有更好的性能。这两种策略对候选网络分区进行了不同的更改。制定了动态社区演化的连续性度量，以比较两个连续时间步长的动态网络社区的相似性。在合成数据集和实际数据集上的实验证明，本文提出的方法比现有方法具有更好的性能。这两种策略对候选网络分区进行了不同的更改。制定了动态社区演化的连续性度量，以比较两个连续时间步长的动态网络社区的相似性。在合成数据集和实际数据集上的实验证明，本文提出的方法比现有方法具有更好的性能。