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This paper studied the discovery of social topics in social networks using an improved label propagation algorithm. To address the problem of traditional algorithms easily falling into local optima, it selected neighbor nodes during label propagation based on the similarity between nodes. To solve the randomness issue in label updates of traditional algorithms, it used the node influence to update labels by incorporating the opinion interaction process from the HK opinion dynamics model. The experimental results show that the proposed method, in the best case (k=0.9), improves stability by 31% and modularity by 78% compared to the original algorithm and outperforms several other improved algorithms. It demonstrates that the proposed algorithm performs better in discovering topic communities in social opinion networks compared to the original algorithm and other improved algorithms. [ABSTRACT FROM AUTHOR]

Facial image datasets are particularly vulnerable to challenges such as lighting variations and occlusion, which can complicate data classification. Semi-supervised learning, using a limited amount of labeled facial data, offers a solution by enhancing face classification accuracy while reducing manual labeling efforts. The Label Propagation Algorithm (LPA) is a commonly used semi-supervised algorithm that employs Radial Basis Function (RBF) to measure similarities between data nodes. However, RBF struggles to capture complex nonlinear relationships in facial data. To address this, an improved LPA is proposed that integrates Shared Nearest Neighbor (SNN) to enhance the correlation measurement between facial data and RBF. Three known datasets were considered: FERET, Yale, and ORL. The experiments showed that in the case of insufficient label samples, the accuracy reached 89.76%, 92.46%, and 81.48%, respectively. The proposed LPA enhances clustering robustness by introducing 128 dimensional facial features and more complex similarity measurement. The parameter of similarity measurement can be adjusted based on the characteristics of different datasets to achieve better clustering results. The improved LPA achieved better performance and face clustering effectiveness by enhancing robustness and adaptability. [ABSTRACT FROM AUTHOR]

Behavioral representations of the patients with depression reflect the clinical features and condition of the patients, therefore it is beneficial for disease diagnosis. However, in the construction of current depression lexicon, the correlation between the behavioral features and the condition of patients in depression texts is overlooked, resulting in incompleteness of the lexicon information. To address this problem, a domain lexicon construction, oriented to behavioral features in depression. was proposed which aimed to extend the domain lexicon's coverage of emotional expressions. Firstly, the seed word sets of sentiment and behavior were constructed by the TF-IDF algorithm respectively, the word set of sentiment was obtained by calculating PMI similarity between the seed word set of sentiment and the existing sentiment lexicon Secondly, the seed words of behavioral were labeled based on correspondence between behavioral features and the condition of patients, and further inputted into WoBERT with depression texts to separately generate dynamic word vectors. In addition, the candidate word set was acquired by calculating the similarity between the seed word set of behavioral and depression texts In addition,based on the similarity between words, the semantic graph was constructed to obtain the word set of behavioral features by label propagation algorithm. Finally, the emoticons with negative emotions on Weibo were collected to build the word set of emoticons. The word set of sentiment, the word set of behavioral features and the word set of emoticons were integrated into the Chinese Depression Domain Lexicon. Experimental results show that the constructed lexicon can improve the effect of depression text classification.

Science and technology are highly inheritable undertakings, and any scientific and technological worker can make good progress without the experience and achievements of predecessors or others. In the face of an ever-expanding pool of literature, the ability to efficiently and accurately search for similar works is a major challenge in current research. This paper uses Latent Dirichlet Allocation (LDA) topic model to construct feature vectors for the title and abstract, and the bag-of-words model to construct feature vectors for publication type. The similarity between the feature vectors is measured by calculating the cosine values. The experiment demonstrated that the precision, recall and WSS95 scores of the algorithm proposed in the study were 90.55%, 98.74% and 52.45% under the literature title element, and 91.78%, 99.58% and 62.47% under the literature abstract element, respectively. Under the literature publication type element, the precision, recall and WSS95 scores of the proposed algorithm were 90.77%, 98.05% and 40.14%, respectively. Under the combination of literature title, abstract and publication type elements, the WSS95 score of the proposed algorithm was 79.03%. In summary, the study proposes a robust performance of the literature screening (LS) algorithm based on the LDA topic model, and a similar LS system designed on this basis can effectively improve the efficiency of LS. [ABSTRACT FROM AUTHOR]

We propose a label propagation-based algorithm to extract community structure using a new similarity measure based on centrality and common neighbours. Initially, a distinct label is assigned to each vertex. Along the process, each vertex adopts the label that most of its similar neighbours have. Communities are defined when closely related groups of vertices agree on a single label. Experiments on both real-world and synthetic networks show that our algorithm has better stability than the label propagation algorithm and finds communities with small sizes and indistinct boundaries more effectively than most state-of-the-art community detection methods. [ABSTRACT FROM AUTHOR]

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In today's world, networks play a very important role in people's lives. One of the important issues related to networks is the issue of detecting communities. These communities are also called groups and clusters. Communities include nodes that are closely related to each other. Most of the nodes that are members of a community have common properties. In social networks, it is important to detect the community in order to analyze the network and it is a very important tool to understand the information of the network and its structure. Studying community detection has garnered significant interest in last few years, leading to the development of numerous algorithms in this area. this research, we used the gray wolf meta-heuristic algorithm and improved it with operators such as mutation, combination, and local search, and also improved the final solution of the gray wolf algorithm with the label propagation algorithm to detect communities. Experiments showed that the proposed method has high accuracy and also due to the applied techniques, the problem converges to the best solution very quickly. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the “artifacts” class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method.

A challenging issue in complex network analysis is overlapping community detection, which has attracted many studies. Label Propagation Algorithm (LPA) is one of the famous studies to detect communities. But it has some weaknesses such as using local information and randomly choosing the sequences of processing nodes. We introduce Evolutionary Label Propagation Algorithm (ELPA) to solve these problems and improve accuracy. ELPA uses an intelligent search instead of randomly processing nodes and fuses local and global perspectives. The proposed ELPA is compared with several state-of-the-art algorithms on synthetic and real-world networks with different sizes, densities, and complexities. The results indicate that ELPA provides better results on most of the test instances. Therefore, ELPA is an accurate and efficient algorithm for detecting overlapping communities. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of overlapping community detection, this paper proposed a LPANNI optimization algorithm CLPANNI (Cycle Label Propagation Algorithm with Neighbor Node influence) based on cycle structure by mining the minimum circle information of nodes, measuring the importance of nodes according to the circle ratio index and updating labels in ascending order, the algorithm increases the stability of label propagation process, and receives the labels transmitted by neighbor nodes according to the influence of neighbor nodes. With four benchmark algorithms in NMI＿ LFK, NMI＿ Through the test and comparison under MGH and Mov indicators, CLPANNI algorithm performs well in the accuracy of community discovery. Experimental results show that the algorithm can effectively detect the overlapping community structure of the network, find the close sub clusters of the network, and the identified community distribution is closer to the real network structure. [ABSTRACT FROM AUTHOR]

Semi-Supervised Graph Learning (SSGL) aims to predict massive unknown labels based on a subset of known labels within a graph. Recently, graph neural network, one of the most popular SSGL approaches, has garnered considerable research interest and achieved remarkable progress. However, many of these methods perform unsatisfactorily with limited labeled data. Graph contrastive learning (GCL), which utilizes unlabeled data to generate supervision, partially addresses this issue but does not fully exploit label information. To address this challenge, we propose SSGL algorithm, Semi-supervised Graph Contrastive Learning with Confidence Propagation Algorithm (SGCL). SGCL comprises two stages of contrastive learning. In the first stage, we employ unsupervised contrastive learning to initialize the model with graph augmentation. In the second stage, in order to fully leverage known labels and graph structure, we incorporate supervised contrastive learning which utilizes supervision signals obtained from confidence propagation algorithm. By combining supervised contrastive learning and unsupervised contrastive learning, the embedding quality and the classification accuracy can be further enhanced. At last, comprehensive experiments demonstrate that SGCL outperforms the best baseline method by an average of 2.23% across six datasets, highlighting the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The detection of community structure plays an important role in understanding the properties and characteristics of complex networks. The label propagation algorithm (LPA) emerges as a popular community detection method, due to its simplicity and low computational cost. Nonetheless, the LPA is not without its limitations so that the Semi Synchronous Constrained Label Propagation Algorithm (SSCLPA) is a modified LPA that implements various constraints to ameliorate the stability of the LPA. Aside from giving accurate and deterministic detection, it can avoid trivial detection. In this paper the SSCLPA is extended into weighted and directed networks, so that nodes which fulfill certain conditions are updated separately at the end of the algorithm. Furthermore, some modifications are performed on the propagation processes in the SSCLPA. These new features and modifications improve the time efficiency of the SSCLPA with only marginal loss in the quality of the detection. Our proposed method is tested and compared to the other community detection methods in various benchmark and real-world networks. The results showed that the proposed method is a well-balanced method with features that takes into account the stability, quality and time efficiency of the detection. [ABSTRACT FROM AUTHOR]

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Health Smart Home (HSH) is an important part of smart city. This technology provides a new kind of remote medical treatment, and can effectively alleviate the shortage of medical resources caused by aging population and help elderly people live at home more safely and independently. Activity recognition is the core of Health Smart Home. However, constructing activity recognition models usually requires a large amount of labeled data, which imposes a heavy burden on manual labeling. In this article, the authors propose an activity labeling approach based on a graph-based semi-supervised learning algorithm. This approach can divide the raw sensor event sequence without any label information into appropriate segments. Consecutive sensor events that occurred in a same activity are grouped into a same segment. In addition, this approach requires only a small number of manually labeled segments to complete the labeling of the remaining large number of unlabeled segments, thereby greatly reducing the burden of manual labeling. After that, all the labeled data can be further used for activity recognition in smart homes. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed activity labeling approach.

The first task of parallel power system restoration is to sectionalize the blackout system into multiple subsystems. This paper applies the complex network community discovery theory into this sectionalizing problem and proposes an improved label propagation algorithm-based sectionalizing method considering the system topology and operation before blackouts. Firstly, each blackstart (BS) unit bus is marked with a different subsystem label. A label propagation matrix is calculated based on the active power of branches before the blackout. Then, to avoid the label oscillation, a novel label impact strategy considering the influence of the bus label itself is developed to improve the label propagation matrix. The buses' labels propagate to neighboring buses as the improved label propagation matrix until they do not change. The initial sectionalizing strategy is obtained through classifying the buses with the same label in the same subsystem. Finally, the sectionalizing constraints are used to evaluate the feasibility of the initial strategy. For the initial strategy that does not satisfy the constraints, a refining method to minimize the absolute value of active power exchange among subsystems is proposed to determine the final sectionalizing strategy based on it. Case studies on IEEE 39-bus and IEEE 118-bus test systems verify the effectiveness of the proposed method. The results indicate that the proposed method has advantages in creating strongly connected subsystems.

With the development of mobile Internet, it is changing the way we communicate with others. Internet media have gradually become the main mobile crowdsourcing applications for information dissemination and user communication, including online news and social networks. However, the potential business opportunities have stimulated the emergence of a large number of spammers, who release false speech, advertisements, pornographic contents, and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, the research on the identification technology of spammers has been carried out extensively. However, the traditional technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the label propagation algorithm (LPA), making full use of the user comment behaviors and contents. First of all, we collect a large amount of news and comments from NetEase News and label some users in the data as spammers or normal users manually to construct a labeled dataset. Then, a set of behavioral and semantic features are extracted and quantified from the user comment behaviors and comment contents by statistical analysis. Next, we propose the identification technology based on the LPA. Finally, the set of feature values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine the most effective combination of features and improve the technology. The results show that the technology proposed in this paper involves a lower data cost but a better identification effect than some traditional technologies based on the supervised classifier.

Many practice problems can be transformed into complex networks, and complex network community discovery has become a hot research topic in various fields. The classic label propagation algorithm (LPA) can give community partition very quickly, but stability of the algorithm is poor due to random label propagation. To solve this problem, community leader principle is built and transition probability is introduced, a label propagation algorithm based on community leader and transition probability (CTLPA) is proposed. CTLPA selects threatened leaders and their communities according to the community leader principle, and uses the transition probability and the degree of the leader to jointly control the order for community merger, so that the threatened leader continuously devours the communities that threaten him, until a preliminary community partition is formed. To further reduce the number of community, in CTLPA, based on the characteristic of the community structure: close relationship within the community and sparse relationship outside the community, the closest communities are merged, until the final community partition is obtained. The CTLPA is compared with other five classic algorithms on LFR artificially generated networks and several real data sets. The experimental results show that CTLPA is robust in community partition, it always gives the same community partition, while the LPA will give different results from multiple independent runs. The number of community partition and the normalized mutual information (NMI) of the CTLPA are the best in most cases. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of open source software bug localization, this paper proposed an open source soft. ware bug localization method based on twice propagation algorithm (TPA). It combined the restart random walk with the label propagation algorithm based on the information retrieval method. The specific location of the bug is analyzed from the three aspects: the call relationship, module risk and historical bug report of each module in the open source software. The bug localization is divided into two parts: risk propagation and label propagation. Through the similarity and calling relationship between the software modules, combined with the history of modified reports, it evaluated the module' s risk. It constructed the bug label through the modified bug reports, and propagated the software label from it. In the Eclipse 3.1 bug report data set, the top-N rank and MRR indicators are higher than the similar bug location methods. Experiments show that the TPA method has higher accuracy. [ABSTRACT FROM AUTHOR]

It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge different communities and gain access to multiple sources of information flow. In this paper, we devised a structural hole detection algorithm, known as the Conductance–Degree structural hole detection algorithm (CD-SHA), which computes the conductance and degree score of a vertex to identify the structural hole spanners in social networks. Next, we proposed an improved label propagation algorithm based on conductance (C-LPA) to filter the jamming nodes, which have a high conductance and degree score but are not structural holes. Finally, we evaluated the performance of the algorithm on different real-world networks, and we calculated several metrics for both structural holes and communities. The experimental results show that the algorithm can detect the structural holes and communities accurately and efficiently. [ABSTRACT FROM AUTHOR]

Community detection is one of the most important topics in complex network analysis. Among a variety of approaches for detecting communities, the label propagation algorithm (LPA) is the simplest and time-efficient approach. However, the original label propagation algorithm is not stable due to the randomness in its propagation process. In this paper, we propose a graph-based label propagation algorithm (GLPA) to detect communities incorporating the node similarity and connectivity information during the propagation of the labels. First, we define node similarity between adjacent nodes, and change each node's label to that of its most similar neighbor node. Based on the label propagation process, GLPA constructs a label propagation graph to get candidate communities. Then, GLPA calculates the connected components of the label propagation graph. Each connected component is treated as a candidate community in the next step. Second, GLPA constructs a weighted graph to obtain final communities, in which each connected component are treated as a super-node, and the number of edges lying between the corresponding components as the weight of edges. We compute the merging factor for each node in the weighted graph and merge super nodes with higher merging factor to its most similar node iteratively to reach the maximum complementary entropy. Compared with 8 other classical community detection algorithms on LFR artificial networks and 12 real world networks, the proposed algorithm GLPA shows preferable performance on stability, NMI, ARI, modularity. [ABSTRACT FROM AUTHOR]

Analyzing the solution space structure and evolution of 3-satisfiability (3-SAT) problem is an important way to study the difficulty of the solving satisfiability (SAT) problem. However, there is no unified analysis model for the spatial structure and evolution of solutions under different constraint densities. The analysis of different phase transition points and solution regions is based on different metric analysis models. The solution space of 3-SAT problem is obtained by planting strategy and belief propagation. According to the distribution of the influence of frozen variables on the solution, a label propagation algorithm based on planting strategy is proposed, is used to find the solution cluster, and then the structure entropy is used to measure its structure information. The structure entropy analysis model of 3-SAT problem solution space is established, and the unified analysis framework of solution space evolution and satisfiability phase transition is given. The experimental results show that the model is effective and can accurately analyze the evolution process of solution space and satisfiability phase transition, and verify the accuracy of interference phase transition point threshold predicted by long-range frustration theory.

Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best. [ABSTRACT FROM AUTHOR]

In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of internal edges (the edges between the vertices within the same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting community structure helps data scientists to extract meaningful information from networks and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. Yet, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. Label propagation algorithm (LPA), a fast random-based algorithm, can easily detect communities in big graphs, but its accuracy compared to other algorithms is low. In this paper, we have improved LPA accuracy, and to achieve this goal, we propagate label based on the Local Edge Betweenness score. The proposed algorithm, named Weighted Label Propagation based on Local Edge Betweenness, is able to identify distinct communities in both the real-world and artificial networks. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable. [ABSTRACT FROM AUTHOR]

Overlapping community structure is a significant feature of large-scale complex networks. Some existing community detection algorithms cannot be applied to large-scale complex networks due to their high time or space complexity. Label propagation algorithms were proposed for detecting communities in large-scale networks because of their linear time complexity, however most of which can only detect non-overlapping communities, or the results are inaccurate and unstable. Aimed at the defects, we proposed an improved overlapping community detection algorithm, LPANNI (Label Propagation Algorithm with Neighbor Node Influence), which detects overlapping community structures by adopting fixed label propagation sequence based on the ascending order of node importance and label update strategy based on neighbor node influence and historical label preferred strategy. Extensive experimental results in both real networks and synthetic networks show that, LPANNI can significantly improve the accuracy and stability of community detection algorithms based on label propagation in large-scale complex networks. Meanwhile, LPANNI can detect overlapping community structures in large-scale complex networks under linear time complexity. [ABSTRACT FROM AUTHOR]

Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithms, which use task (protein functions) similarity information as a bias to speed up learning, we show that dissimilarity information enforces separation of rare class labels from frequent class labels, and for this reason is better suited for solving unbalanced protein function prediction problems. We support our claim by showing that a multitask extension of the label propagation algorithm empirically works best when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. Moreover, the experimental comparison carried out on three model organism shows that our method has a more stable performance in both "protein-centric" and "function-centric" evaluation settings. [ABSTRACT FROM AUTHOR]

In complex networks, there are a huge number of overlapping community structures, and those structures have been an increasing concern in recent years. The label propagation algorithm (LPA) family is a kind of mainstream method that discovers different community structures in complex networks. But these methods often struggle with stability and feasibility, as well as issues sensitive to network size. In this paper, to solve the above problems and improve the detection accuracy, we present a fast label propagation algorithm (FLPA) based on node influence and label weight. In FLPA, we first use the graph compression technique to reduce the network size. Then, we propose a new node influence calculation method and fuse it with the α -path similarity to precisely control the label weight in the label propagation stage. Through the above process, FLPA makes the labels that are propagated and received by nodes more reasonable and further improves the detection accuracy. Finally, we restore the network and make the compressed node belong to the same community as its corresponding super node. Experimental results on 10 real and 58 synthetic networks indicate that FLPA is suitable for detecting overlapping community structures regardless of network scale and accomplishes better than state-of-the-art methods on stability and feasibility. [ABSTRACT FROM AUTHOR]

It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge different communities and gain access to multiple sources of information flow. In this paper, we devised a structural hole detection algorithm, known as the Conductance–Degree structural hole detection algorithm (CD-SHA), which computes the conductance and degree score of a vertex to identify the structural hole spanners in social networks. Next, we proposed an improved label propagation algorithm based on conductance (C-LPA) to filter the jamming nodes, which have a high conductance and degree score but are not structural holes. Finally, we evaluated the performance of the algorithm on different real-world networks, and we calculated several metrics for both structural holes and communities. The experimental results show that the algorithm can detect the structural holes and communities accurately and efficiently.

As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.