## 1 Introduction
Additionally, for large-scale networks, inductive learning methods such as GraphSAGE [34] achieve efficient training by sampling neighboring nodes. Although deep learning methods have demonstrated excellent performance in community detection, their effectiveness relies heavily on high-quality training data. Moreover, their black-box nature makes community partitioning results difficult to interpret, and they often exhibit limited generalizability on sparse networks. To address the advantages and disadvantages of existing algorithms, we propose a new label propagation algorithm for network node importance, called the DegreeRank Label Propagation (DRLP) Algorithm. By explicitly defining node importance metrics and label update rules, DRLP maintains algorithmic efficiency while achieving interpretable community partitioning logic. Specifically, by defining a new update rule and fixing the order of nodes for the label update, DRLP improves both the robustness of the traditional LPA and community detection accuracy. Firstly, DRLP generates a specific metric indicator to reflect the importance of nodes in the whole network topology based on the local and global structural characteristics of each node. Secondly, DRLP repeats the process of labeling each node according to a new label updating rule until the community structure of the whole network is detected. Throughout the algorithm phase, we determine the update order of nodes in descending order of node importance to reduce the randomness of the algorithm; meanwhile, in the label update rules, we consider that the update of node labels is determined by both the authority of the neighboring nodes (node importance) and the closeness between the node and the neighboring nodes. Finally, DRLP divides all nodes with the same label into a community. The main contributions of this paper are summarized as follows:  
* We propose a new method to reflect the correlation between network nodes that can efficiently find the shortest path and decrease the time complexity. We obtain a way to detect the correlation between nodes more accurately when analyzing the influence of nodes such that network topology information is kept. * We introduce a damping factor that reflects the affinity between nodes, which can be adjusted to affect the change of affinity between nodes caused by unexpected events, which is more in line with interpersonal interactions in real social networks. * We present a new node importance metric to solve the random problem of existing similar algorithms by globalizing the local characteristics of nodes. This metric provides a more accurate assessment of node importance, which can improve the accuracy of community partition. * We propose a modified label propagation strategy. We emphasize the influence of neighboring nodes on the target node when selecting a label, which ensures the maximization of influence propagation within each community during the community partitioning process. This method is used to solve the problem of random selection of nodes, which enhances the efficiency and feasibility of the algorithm. * We perform simulations to verify the stability and superiority of the DRLP algorithm on real network datasets and artificial synthetic networks. We also validate the higher accuracy and better performance in terms of NMI and modularity than other methods. The rest of this paper is organized as follows. Section 2 summarizes related work. Then, in Section 3, we introduce the main ideas and detailed process of our algorithm, and we also analyze the complexity of the proposed algorithm. We introduce the relevant parameter settings of our algorithm and discuss the experimental results of the real-world and synthetic networks in Section 4. Finally, Section 5 provides conclusions and perspectives.