Label Propagation Approach Research Articles

Assessment of Mars Analog Habitation Plans Using Network Analysis Methodologies

Space institutions place humans in analog environments that closely match the target environment, such as the Moon or Mars, to study space habitation. Seven Mars analog missions consisting of habitation units with published designs were selected for analysis using graph data science methodologies to provide insight into how their designers solved the challenge of living in an isolated, constrained, and distant environment. Social Network Analysis (SNA) and Label Propagation Approach (LPA) are utilized to examine the relationships between adjacent functional areas of habitations. The paper presents the final documentation that will be used as a design framework to generate layouts for a habitation project.

Detecting Overlapping Communities in Complex Networks: An Evolutionary Label Propagation Approach

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.

Semi-supervised label propagation for multi-source remote sensing image change detection

Remote sensing image change detection remains a challenging task. Most existing approaches are based on fully supervised learning, but labeled data are so scarce for change detection. It is difficult to exhibit high detection performance with a limited amount of labeled data. In this paper, we propose a semi-supervised Label Propagation (SSLP) approach for multi-source remote sensing image change detection. First, a clustering label propagation (CLP) method is designed to cluster pre and post images, respectively, and assign pseudo labels to unlabeled pixel pairs that have similar mapping relationships to labeled pixel pairs. Second, a pixel density metric is investigated to filter out the data with low density and retain the data with high density, which can ensure the reliability of the propagated data. Third, a secondary expansion method based on pixel neighborhood is used to generate enough training data for training a classifier. Finally, the effectiveness of SSLP is validated on three real datasets by comparing to other related methods.

Adaptive and structured graph learning for semi-supervised clustering

This paper proposes a new method for semi-supervised clustering of data that only contains pairwise relational information. Specifically, our method simultaneously learns two similarity matrices in feature space and label space, in which similarity matrix in feature space learned by adopting adaptive neighbor strategy while another one obtained through tactful label propagation approach. Moreover, the above two learned matrices explore the local structure (i.e., learned from feature space) and global structure (i.e., learned from label space) of data respectively. Furthermore, most of the existing clustering methods do not fully consider the graph structure, they can not achieve the optimal clustering performance. Therefore, our method forcibly divides the data into c clusters by adding a low rank restriction on the graphical Laplacian matrix. Finally, a restriction of alignment between two similarity matrices is imposed and all items are combined into a unified framework, and an iterative optimization strategy is leveraged to solve the proposed model. Experiments in practical data show that our method has achieved brilliant performance compared with some other state-of-the-art methods.

Developing a label propagation approach for cancer subtype identification problem

Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagationbased approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.

On the use of high-order feature propagation in Graph Convolution Networks with Manifold Regularization

Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called ”GCNs with Manifold Regularization” (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature propagation into each GCN layer. High-order feature propagation over the graph can fully exploit the structured information provided by the latter at all the GCN’s layers. It fully exploits the clustering assumption, which is valid for structured data but not well exploited in GCNs.Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects, faces and digits: Extended Yale, PF01, Caltech101 and MNIST. We will also consider three citation networks. The proposed scheme performs well compared to several semi-supervised methods. With respect to the recent GCNMR approach, the average improvements were 2.2%, 4.5%, 1.0% and 10.6% on Extended Yale, PF01, Caltech101 and MNIST, respectively.

BGRAP: Balanced GRAph Partitioning Algorithm for Large Graphs

The definition of effective strategies for graph partitioning is a major challenge in distributed environments since an effective graph partitioning allows to considerably improve the performance of large graph data analytics computations. In this paper, we propose a multi-objective and scalable Balanced GRAph Partitioning (\algo) algorithm, based on Label Propagation (LP) approach, to produce balanced graph partitions. \algo defines a new efficient initialization procedure and different objective functions to deal with either vertex or edge balance constraints while considering edge direction in graphs. \algo is implemented of top of the open source distributed graph processing system Giraph. The experiments are performed on various graphs with different structures and sizes (going up to 50.6M vertices and 1.9B edges) while varying the number of partitions. We evaluate \algo using several quality measures and the computation time. The results show that \algo (i) provides a good balance while reducing the cuts between the different computed partitions (ii) reduces the global computation time, compared to LP-based algorithms.

Developing a label propagation approach for cancer subtype classification problem.

Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation-based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.

Superpixel-Level Weighted Label Propagation for Hyperspectral Image Classification

As a typical graph-based semisupervised learning technique, the label propagation (LP) approach has gained much attention in recent years. The key to LP algorithms is the propagation capability and efficiency of the similarity matrix, which describes the similarity between two data points. Concerning hyperspectral image which often contains hundreds of thousands of pixels, the corresponding similarity matrix is particularly huge and thus the LP procedure is intractable. Fortunately, superpixel, which can effectively characterize the spatial semantic information of surface objects, provides a reasonable way to solve this problem. In this article, we propose an elaborate superpixel-based weighted LP approach, abbreviated as SuWLP, for hyperspectral image classification. First, the hyperspectral image is oversegmented by the entropy rate segmentation (ERS) method, and the internal consistency of each superpixel can be achieved. Second, a new similarity measure is designed to estimate the similarity between two superpixels, and a superpixel-based similarity matrix can be thus established. Third, after the training samples have been expanded based on the superpixel distribution, a weighted LP technique is designed to propagate the sample label at the superpixel level without any parameter tuning. Finally, the label of each superpixel maps back to the contained pixels. We compared our proposed SuWLP method with several state-of-the-art ones, and experimental results on three real hyperspectral data sets certify the effectiveness and efficiency of the superpixel-level LP strategy.

Identifying Android Malware Using Network-Based Approaches

The proliferation of Android apps has resulted in many malicious apps entering the market and causing significant damage. Robust techniques that determine if an app is malicious are greatly needed. We propose the use of a network-based approach to effectively separate malicious from benign apps, based on a small labeled dataset. The apps in our dataset come from the Google Play Store and have been scanned for malicious behavior using Virus Total to produce a ground truth dataset with labels malicous or benign. The apps in the resulting dataset have been represented using binary feature vectors (where the features represent permissions, intent actions, discriminative APIs, obfuscation signatures, and native code signatures). We have used the feature vectors corresponding to apps to build a weighted network that captures the “closeness” between apps. We propagate labels from the labeled apps to unlabeled apps, and evaluate the effectiveness of the proposed approach using the F1-measure. We have conducted experiments to compare three variants of the label propagation approaches on datasets that include increasingly larger amounts of labeled data. The results have shown that a variant proposed in this study gives the best results overall.

Dynamic Community Detection Based on a Label-Based Swarm Intelligence

The dynamic network tails after the development of the real-world that is essential for particle applications such as traffic flow analyses and social network analyses. The requirement of maximizing the quality of the community structure at current time step and minimizing the difference of the community structure between two successive time steps synchronously brings serious challenges to the dynamic community detection. Some existing approaches (i.e., the multi-objective particle swarm optimization, named as DYNMOPSO) utilize the swarm intelligence pattern to solve such a community detection problem in dynamic networks. Nevertheless, the DYNMOPSO has the deficiency of the undesirable prematurity constringency and monotonicity of particles because of the high choice stress. Thus, a label-based swarm intelligence on the basis of the evolutionary clustering framework is presented for overcoming those disadvantages. The label propagation approach initializes the labels of particles and is used for escaping the prematurity constringency. The crossover and mutation methods are introduced to improve the variety of particles and retain preferable the community structure synchronously. Experiments in synthetical and real networks prove that our algorithm is valid and exceeds state-of-the-art approaches.

A heterogeneous label propagation approach to explore the potential associations between miRNA and disease

BackgroundResearch on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.MethodsIn this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.ResultsHLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.ConclusionsAll the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.

A label propagation approach based on local optimization

Community detection offers an important way to understand the structures and functions of social network. The label propagation algorithm has attracted vast attention since it is very suitable for discovering communities from large-scale networks. However, the algorithm suffers from the instability and inefficiency problem caused by the random policies it adopted. In this paper, we propose a novel label propagation approach based on local optimization to deal with the problem. The approach introduces a pre-propagation mechanism to optimize randomly initialized labels according to special factors, for example, node compactness. After that, it traverses and relabels nodes in the descending order of aggregate influence. The experiment results demonstrate the usefulness and effectiveness of our approach.

Flexible semi-supervised embedding based on adaptive loss regression: Application to image categorization

This paper introduces two graph-based semi-supervised embedding methods for generic classification and recognition tasks. These proposed methods combine the merits of Flexible Manifold Embedding, non-linear graph based embedding, and adaptive loss function. The adaptive loss function seems to be a good choice for reaching a flexible and adaptive regressor in the sense that the effect of outliers is reduced and the regression function is more elastic and more robust. Furthermore, unlike label propagation approaches, the proposed methods provide a data embedding into a space whose dimension is not limited to the number of classes. The adaptive loss function combines the merits of the ℓ1, 2 and ℓ2 matrix norms. It can handle the Laplacian distribution of outliers and the Gaussian distribution of samples with small loss. We provide extensive experiments on eight benchmark datasets in order to study the performance of the proposed methods. These experiments show that the proposed methods can be more discriminative than other state-of-the-art methods.

A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks

One of the most important topics in social network analysis is community detection in dynamic social networks. A variety of approaches exists for detecting communities in dynamic social networks, among which the label propagation algorithm (LPA) is the well-known approach. This approach has made remarkable performance, but still has several problems. One of the difficulties of this approach is the new nodes added to the social network graph in the current snapshot has a very slight chance of creating new communities. In fact, these nodes fall under the influence of existing communities. This drawback decreases the accuracy of community detection in dynamic social networks. We propose a new method based on label propagation approach and the cascade information diffusion model in order to solve this difficulty. Here, the newly proposed method, Speaker Listener Propagation Algorithm Dynamic (SLPAD), Dominant Label Propagation Algorithm Evolutionary (DLPAE) and Intrinsic Longitudinal Community Detection (ILCD) on real and synthetic networks are implemented. The findings indicate that the modularity and Normalized Mutual Information (NMI) and also F1AVG of this proposed method is considerably higher than the earlier available methods in most datasets. Therefore, it can be concluded that the proposed method improves the accuracy of community detection in comparison with other available methods.

Label propagation approach for predicting missing biographic labels in face‐based biometric records

A biometric system uses the physical or behavioural attributes of a person, such as face, fingerprint, iris or voice, to recognise an individual. Many operational biometric systems store the biographic information of an individual, viz., name, gender, age and ethnicity, besides the biometric data itself. Thus, the biometric record pertaining to an individual consists of both biometric data and biographic data. We propose the use of a graph structure to model the relationship between the biometric records in a database. We show the benefits of such a graph in deducing biographic labels of incomplete records, i.e. records that may have missing biographic information. In particular, we use a label propagation scheme to deduce missing values for both binary-valued biographic attributes (e.g. gender) as well as multi-valued biographic attributes (e.g. age group). Experimental results using face-based biometric records consisting of name, age, gender and ethnicity convey the pros and cons of the proposed method.

Discriminative clustering on manifold for adaptive transductive classification

In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manifold learning, discriminative clustering and adaptive classification into a unified model. Also, our method incorporates the adaptive graph weight construction with label propagation. Specifically, our method is capable of propagating label information using adaptive weights over low-dimensional manifold features, which is different from most existing studies that usually predict the labels and construct the weights in the original Euclidean space. For transductive classification by our formulation, we first perform the joint discriminative K-means clustering and manifold learning to capture the low-dimensional nonlinear manifolds. Then, we construct the adaptive weights over the learnt manifold features, where the adaptive weights are calculated through performing the joint minimization of the reconstruction errors over features and soft labels so that the graph weights can be joint-optimal for data representation and classification. Using the adaptive weights, we can easily estimate the unknown labels of samples. After that, our method returns the updated weights for further updating the manifold features. Extensive simulations on image classification and segmentation show that our proposed algorithm can deliver the state-of-the-art performance on several public datasets.

Graph-based semi-supervised classification on very high resolution remote sensing images

Classification of very high resolution (VHR) remote sensing imagery is a rapidly emerging discipline but faces several challenges owing to the huge scale of the pixel data involved, indiscernibility in the traditionally used features to represent various regions, and the lack of available ground truth data. This paper provides a framework which elegantly overcomes these hurdles by providing a novel semi-supervised learning approach which employs multiscale superpixel tessellation representations of VHR imagery. Superpixels are homogeneous and irregularly shaped regions which form the backbone of our approach and are used to derive novel features by learning a decision tree. Our semi-supervised learning approach works on a superpixel graph and seamlessly combines the large margin capability of a support vector machine (SVM) with a graph-based Laplacian label propagation approach to obtain a novel objective function. Further we also provide a self-contained and easily parallelisable linear iterative optimisation approach based on the principle of majorisation-minimisation. We evaluate this approach on four different geographic settings with varying neighbourhood types and draw comparisons with the popular and widely used Gaussian multiple instance learning algorithm. Our results showcase several advantages in accuracy and efficiency, which coupled with the ease of model building and inherently parallelisable optimisation make our framework a great choice for deployment in large scale applications like global human settlement mapping and population distribution, and change detection.

Graph-based semi-supervised classification on very high resolution remote sensing images

Classification of very high resolution (VHR) remote sensing imagery is a rapidly emerging discipline but faces several challenges owing to the huge scale of the pixel data involved, indiscernibility in the traditionally used features to represent various regions, and the lack of available ground truth data. This paper provides a framework which elegantly overcomes these hurdles by providing a novel semi-supervised learning approach which employs multiscale superpixel tessellation representations of VHR imagery. Superpixels are homogeneous and irregularly shaped regions which form the backbone of our approach and are used to derive novel features by learning a decision tree. Our semi-supervised learning approach works on a superpixel graph and seamlessly combines the large margin capability of a support vector machine (SVM) with a graph-based Laplacian label propagation approach to obtain a novel objective function. Further we also provide a self-contained and easily parallelisable linear iterative optimisation approach based on the principle of majorisation-minimisation. We evaluate this approach on four different geographic settings with varying neighbourhood types and draw comparisons with the popular and widely used Gaussian multiple instance learning algorithm. Our results showcase several advantages in accuracy and efficiency, which coupled with the ease of model building and inherently parallelisable optimisation make our framework a great choice for deployment in large scale applications like global human settlement mapping and population distribution, and change detection.

Projective label propagation by label embedding: A deep label prediction framework for representation and classification

Virtually all existing label propagation (LP) approaches estimate the unknown labels of points from the original input space directly, but the transductive results are “shallow” and usually contain unfavorable mixed signs that may decrease the performance of both transductive models and out-of-sample extensions. To solve this issue, we propose a Projective Label Propagation (ProjLP) framework by label embedding, which can deliver more discriminating “deep” labels of samples to enhance representation and classification. ProjLP is originally proposed to improve the transductive prediction results by simultaneously learning a robust projection to remove the unfavorable mixed signs and convert the shallow labels into the discriminating deep ones. By including a regressive reconstruction loss term to correlate the extracted discriminative features with the deep labels, the out-of-sample extension of ProjLP is also presented by delivering a linear neighborhood preserving projection classifier, where the neighborhood preserving power is enabled by sharing the same graph weights in the original embedded deep label space and the approximated label space. Thus, the deep label of each new data can be obtained by embedding it directly onto the classifier. We also show the inclusion method by reconstructing the deep label of new data from the deep labels of its neighbors. To show the deep property of embedded “deep” labels, a multilayer network architecture of our model is illustrated. Simulations on artificial and real-world datasets demonstrate the validity of our techniques for representation and classification, compared with other state-of-the-arts.