## 1 Introduction
[PERSON] et al. [PERSON] et al. (2018) present a node importance label optimization algorithm based on Bayesian networks, which updates the order of nodes based on their importance to optimize the stochasticity of traditional LPA algorithms, but it requires a large amount of prior knowledge in defining the node importance. [PERSON] et al. [PERSON] et al. (2018) use the LPA strategy, allowing a node to contain multiple labels, giving a label confidence function, and updating labels according to the confidence coefficients of different labels. [PERSON] et al. [PERSON] et al. (2018) propose a Deep Walk label optimization algorithm for overlapping community detection. This method utilizes the Deep Walk model to learn the network topology and obtain low-dimensional vector representations of nodes. It constructs a weight matrix by vector dot product operations and detects overlapping communities based on information exchange between nodes. [PERSON] et al. [PERSON] et al. (2018) used the similarity index to assign weights to edges and transformed the label propagation process into the ant colony traversal process according to the ant colony optimization (ACO) algorithm, which transforms the problem of labeling influence into a problem of transfer probability. [PERSON] et al. [PERSON] et al. (2018), in order to improve the robustness of the LPA algorithm, propose an importance metric that combines network locality with the node's global position in the network, but at the expense of increasing the complexity of the algorithm itself and sacrificing the advantages of the LPA algorithm's fast running speed and low time overhead. Similarly, [PERSON] et al. [PERSON] et al. (2018) add multi-step greedy fusion [PERSON] et al. (2018) to optimize the LPA algorithm to improve the robustness of the algorithm and increases the complexity of the algorithm. [PERSON] et al. [PERSON] et al. (2018) extended [PERSON]'s law for electrostatic attraction in physics to social networks, proposing a novel similarity matrix that quantifies node importance through pairwise attraction forces. However, this Coulomb matrix-based approach incurs significant computational overhead in both time and space complexity. In order to solve the problem of the instability and low quality of the attribute graph, [PERSON] et al. [29] generate a weighted graph combining node attributes and topological structure from the attribute graph of nodes with edge connections, so that the detected community is characterized by both structural cohesion and attribute homogeneity, which maintains the original efficiency of the LPA algorithm and reduces the number of iterations. However, most label optimization algorithms tend to consider only local node information while ignoring global importance. Additionally, while some optimization algorithms improve community division results, they still suffer from instability issues. Meanwhile, with the rapid development of deep learning techniques, community detection methods based on Graph Neural Networks (GNNs), such as GCNs [30] and GAT [31], have gradually emerged as a research hotspot. These methods overcome the limitations of traditional approaches that rely on handcrafted features or optimization functions by learning low-dimensional node representations in an end-to-end manner, automatically capturing both global topological and local neighborhood features. CPGC [32] effectively detects overlapping and non-overlapping communities by integrating representation learning with clustering, improving graph convolution operations, and introducing community perspective similarity, thereby leveraging both attribute and structural information. However, challenges remain in computational efficiency and scalability. GEAM [33], a graph-enhanced attention model designed for multiplex networks, enhances community detection accuracy by effectively integrating cross-layer semantic information via inter-layer contrastive learning, a self-attention adaptive fusion mechanism, and an edge density-driven module.