To address the problem of poor stability and low accuracy of community division caused by the randomness in the traditional label propagation algorithm (LPA), a community discovery algorithm that combines seed node influence and neighborhood similarity is proposed. Firstly, the K-shell values of neighbor nodes are combined with clustering coefficients to define node influence, the initial seed set is filtered by a threshold, and the less influential one in adjacent node pairs is removed to obtain the final seed set. Secondly, the connection strengths between non-seed nodes and seed nodes are defined based on their own weights, distance weights, and common neighbor weights. The labels of non-seed nodes are updated to the labels of seed nodes with which they have the maximum connection strength. Further, for the case that the connection strengths between a non-seed node and multiple seed nodes are the same, a new neighborhood similarity combining the information between the two types of nodes and their neighbors is proposed, thus avoiding the instability caused by randomly selecting the labels of seed nodes. Experiments are conducted on six classic real networks and eight artificial datasets with different complexities. The comparison and analysis with dozens of related algorithms are also done, which shows the proposed algorithm effectively improves the execution efficiency, and the community division results are stable and more accurate, with a maximum improvement in the modularity of about 87.64% and 47.04% over the LPA on real and artificial datasets, respectively.

针对传统标签传播算法(LPA)随机性导致社区划分稳定性差、准确性低的问题，提出一种结合种子节点影响力和邻域相似度的社区发现算法。首先，将邻居节点的K壳值与聚类系数相结合来定义节点影响力，通过阈值对初始种子集进行过滤，去除相邻节点对中影响力较小的种子集，得到最终种子集。其次，根据自身权重、距离权重和公共邻居权重定义非种子节点和种子节点之间的连接强度。非种子节点的标签被更新为与其具有最大连接强度的种子节点的标签。进一步，针对非种子节点与多个种子节点之间的连接强度相同的情况，结合两类节点及其邻居之间的信息，提出了一种新的邻域相似度，从而避免了随机选择带来的不稳定性。种子节点的标签。在六个经典的真实网络和八个不同复杂度的人工数据集上进行了实验。与数十种相关算法进行对比分析，表明该算法有效提高了执行效率，社区划分结果稳定且更加准确，模块化程度最高分别比传统算法提高了约87.64%和47.04%。分别在真实数据集和人工数据集上的 LPA。