Deep Laplacian Coordinates: End-to-end deeply guided anisotropic diffusion for COVID-19 pulmonary lesion segmentation.

Semi-supervised learning can leverage both labeled and unlabeled samples simultaneously to improve performance. However, existing methods often present the following issues: (1) The emphasis of learning is put on either the similarity structures or the regression losses of data, neglecting the interaction between them. (2) The similarity structures among boundary samples might be unreliable, which misleads label propagation and impairs the performance of models on out-of-sample data. (3) They often involve the inverses of high-order matrices, making them inefficient in computation. To overcome these issues, we propose a scalable semi-supervised learning framework with Discriminative Label Propagation and Correction (DLPC), which collaboratively exploits the regression losses and similarity structures of data. Particularly, each sample is projected onto the independent class labels associated with nonnegative adjustment vectors rather than the propagated labels, such that the distances between samples from different classes are naturally enlarged, making regression losses more effective for boundary samples. Benefiting from this, the regression losses can guide the propagation of labels in boundary areas. Thus, the label information is first propagated through dynamically optimized graph structures and then corrected by the regression losses, effectively improving the quality of labels and facilitating feature projection learning. Furthermore, an accelerated solution has been developed to reduce the computational costs of DLPC on sample scales, thereby making it scalable to relatively large-scale problems. Moreover, the proposed DLPC can not only be applied to single-view scenarios but also extended to multi-view tasks. Additionally, an optimization strategy with fast convergence has been presented for DLPC, and extensive experiments demonstrate the effectiveness and superiority of DLPC over state-of-the-art competitors.

Robust Orthogonal NMF with Label Propagation for Image Clustering

Simultaneous feature and label propagation for multi-view graph convolutional network

GVE-LPA and GSL-LPA: High-speed and internally-connected label propagation on multicore systems

ABSTRACT We propose an incremental similarity‐based label propagation algorithm (DLPA‐S) for detecting dynamic community structures. As the network evolves, the method efficiently updates the communities over time via local label updates driven by changes in network topology—including edge and vertex additions or removals—and vertex similarity. This incremental approach significantly reduces computational cost while preserving accuracy in capturing community evolution. We evaluate DLPA‐S using a comprehensive set of quality metrics that assess both the structural properties of the network and the agreement between detected communities and ground‐truth partitions. Experiments are conducted on synthetic and real‐world dynamic networks, varying key graph characteristics such as the number of vertices and the average degree, as well as across diverse community scenarios. The results show that DLPA‐S consistently achieves stable and high‐performing results, maintains high NMI and F1 scores, ensures strong internal connectivity, clear community separability, and avoids disconnected communities, while remaining computationally efficient.

Domain adaptation methods have been extensively studied for rolling bearing fault diagnosis under various conditions. However, some existing methods only consider the one-way embedding of original space into a low-dimensional subspace without backward validation, which leads to inaccurate embeddings of data and poor diagnostic performance. In this paper, a rolling bearing fault diagnosis method based on multi-source domain joint structure preservation transfer with autoencoder (MJSPTA) is proposed. Firstly, similar source domains are screened by inter-domain metrics; then, the high-dimensional data of both the source and target domains are projected into a shared subspace with different projection matrices, respectively, during the encoding stage. Finally, the decoding stage reconstructs the low-dimensional data back to the original high-dimensional space to minimize the reconstruction accuracy. In the shared subspace, the difference between source and target domains is reduced through distribution matching and sample weighting. Meanwhile, graph embedding theory is introduced to maximally preserve the local manifold structure of the samples during domain adaptation. Next, label propagation is used to obtain the predicted labels, and a voting mechanism ultimately determines the fault type. The effectiveness and robustness of the method are verified through a series of diagnostic tests.

Community detection is crucial in the analysis of social networks. Its main goal is to find groups of users densely connected among them, and sparsely connected between themselves. There are so many algorithms which makes it difficult for researchers to choose the best one for a specific dataset. In this paper, we provide a thorough comparison between five Community Detection (CD) algorithms- Girman-Newman (GN), Clauset-Newman-Moore (CNM), Label Propagation Algorithm (LPA), Louvain and Leiden. To evaluate in real social network datasets like Zachary's Karate Club, Dolphin networks and bigger ones such as Facebook, Twitter, LinkedIn along with citation networks we used different metrics modularity and NMI. We employed Normalized Mutual Information (NMI) to quantify the agreement of detected communities with their true ground-truth score and modularity for evaluating the overall quality of partitions given by diverse methods. Our experiments show that greedy and modularity-optimization algorithms are particularly well-suited. Notably, the Leiden algorithm had a better modularity value than Louvain (Q = 0.9141 and Q = 0.9051 of LinkedIn Network respectively) in most of the dataset. The NMI plot provided more explanation about Clauset-Newman-Moore (CNM), Louvain and Label Propagation Algorithm (LPA)which are in good agreement on community detection and their NMI score. These results would allow us to choose more rationally among the different community detection algorithms for social network analysis, by providing accurate quantitative benchmarks.

Hypergraph induced semi-supervised orthogonal nonnegative matrix factorization with label and constraint propagation

Modeling shared e-micromobility as a label propagation process for detecting overlapping communities

LapSDNMF: Label propagation assisted soft-constrained deep non-negative matrix factorisation for semi-supervised multi-view clustering

This study investigates the structural organization of an email communication network constructed from the SNAP Enron dataset, where nodes represent individual email addresses and edges correspond to communication links between them. Communities within the network were identified using the Label Propagation Algorithm (LPA), yielding 35 distinct groups. To evaluate the structural coherence and significance of these communities, we integrated two complementary analytical frameworks: Persistent Homology, from Topological Data Analysis (TDA), and Modularity, a key metric in network theory. Persistent homology was utilized to detect enduring topological features—such as connected components, loops, and voids—that characterize the intrinsic structure of each community across varying filtration scales. Modularity analysis, in turn, quantified the relative density of intra- and inter-community connections. Combining these approaches enabled the classification of communities as non-significant, significant, influential, or highly influential. The findings reveal a strong correlation between persistent topological features and high modularity scores, offering deeper insights into the stability, cohesion, and influence of communities in large-scale social communication networks.

Traditional label propagation algorithms (LPA) exhibit instability and poor accuracy in community discovery, primarily due to random node selection, uncertain label update sequences, and neglect of node importance variations. We present GELPA-OCD (overlapping community discovery based on graph embedding and label propagation algorithm), an overlapping community discovery algorithm that integrates graph embedding with label propagation to address these limitations. Our approach introduces a multidimensional node importance assessment strategy and employs Node2vec graph embedding to represent nodes as low-dimensional vectors, effectively capturing network structure features. The algorithm employs similarity-based weight factors to guide label propagation and implements adaptive filtering mechanisms to enhance effectiveness. We conduct experiments on both real and artificial datasets. Using EQ, NMI , and F1-score as evaluation metrics, the experimental results show that the proposed algorithm effectively reduces randomness and uncertainty in node selection and label updating processes, achieving more stable and accurate overlapping community discovery.

Semi-supervised density-based clustering (SDC) is a density-based clustering technique that integrates both labeled and unlabeled data to improve the accuracy and robustness of cluster assignments. However, many SDC algorithms rely on global parameters to distinguish core from non-core points, which may not be effective for clusters with varying densities or complex shapes. To address this limitation, we propose the S emi-supervised D ensity-based C lustering algorithm with G ranular balls (SDCG), which operates in three phases. First, we construct a set of granular balls to perform an initial segmentation of the data, leveraging the available labeled data, with points within the same granular ball being considered similar. Next, we introduce a strategy that combines coverage and specificity to adjust the granularity of each granule, ensuring that points within the same granule belong to the same cluster. Finally, we employ an adaptive label propagation mechanism based on mutual nearest-neighbor voting, where non-core points are assigned labels according to the highest-voting labels from their mutual nearest neighbors. Overall, SDCG is parameter-free and is able to adaptively perform clustering, improving the performance of the algorithm. Experimental comparisons on both synthetic and real datasets show that our method outperforms existing approaches in terms of efficiency and accuracy, particularly for datasets with varying densities and complex shapes.

Community structures are common features of many real-world networks, and community detection is necessary to understand how these networks are organized. Various approaches have been devised for community detection, with each providing varying degrees of both accuracy and structural understanding. One of them, the Label Propagation Algorithm, is so common because it is simple and computationally cheap. Nevertheless, it does not usually reach great modularity and yields inaccurate community counts and structures in real-world networks. This is mostly due to its naive criteria of selecting the neighbor nodes when it comes to label propagation. To tackle the issue, we developed an adjusted algorithm, which we call Embedding-based Label Propagation (ELP), a hybrid between LPA and node embedding that allows us to combine both local connectivity and global structural data. ELP update step takes into consideration not only the local neighborhood, as in conventional LPA, but also embedding-based similarities to inform more productive neighbor selection. We tested ELP on popular benchmark datasets such as Karate Club, Dolphins, Football, Polbooks, and LFR synthetic networks and compared its results with LPA and other well-established algorithms. The empirical findings show that ELP can always perform better in modularity, NMI and NF1 scores, but it is also scalable to large and complex networks. These results can be used to identify ELP as an effective and powerful method of community-finding in real and artificial-world scenarios.

Graph representations for real-world social networks in the past have missed two important elements: (i) the multiplexity of connections and (ii) representing time. This paper presents a dynamic heterogeneous graph representation for social networks that includes time in every component of the graph, that is, nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four time-dependent queries and a multiclass classification problem that cannot easily be handled in conventional homogeneous graph representations. As a proof of concept, we present a detailed representation of a relatively new social media platform (Steemit), which we use to illustrate both the dynamic querying capability, as well as a prediction task using label propagation algorithm (LPA). We also present temporal social media meta-paths to generalize the LPA to dynamic heterogeneous graph structures, that is, Meta-paths + LPA. To validate and compare our proposed method, we conduct an experiment using three benchmark data sets and show that our proposed method outperforms almost all four state-of-the-art algorithms in category prediction task by at least 13.79% accuracy. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0274 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0274 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

This work introduces a dynamic heterogeneous graph representation that integrates time into both nodes and edges, enabling a more accurate modeling of multiplex and evolving relationships in social networks. We further propose Meta-paths + LPA, an extension of the Label Propagation Algorithm that incorporates temporal meta-paths for improved classification on dynamic heterogeneous structures. The framework is demonstrated through a case study on Steemit, showcasing its ability to handle complex time-dependent queries and prediction tasks.

A representation learning-based time series label propagation for smart grid attack detection

Robust label propagation based on prior-guided cross domain data augmentation for few-shot unsupervised domain adaptation

MFLP: Overlapping community detection by multi-level fast label propagation