Adjacency Graph Research Articles

Using networks to analyze and visualize the distribution of overlapping genes in virus genomes.

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may increase the information content of compact genomes or influence the creation of new genes. Here we report a global comparative study of overlapping open reading frames (OvRFs) of 12,609 virus reference genomes in the NCBI database. We retrieved metadata associated with all annotated open reading frames (ORFs) in each genome record to calculate the number, length, and frameshift of OvRFs. Our results show that while the number of OvRFs increases with genome length, they tend to be shorter in longer genomes. The majority of overlaps involve +2 frameshifts, predominantly found in dsDNA viruses. Antisense overlaps in which one of the ORFs was encoded in the same frame on the opposite strand (-0) tend to be longer. Next, we develop a new graph-based representation of the distribution of overlaps among the ORFs of genomes in a given virus family. In the absence of an unambiguous partition of ORFs by homology at this taxonomic level, we used an alignment-free k-mer based approach to cluster protein coding sequences by similarity. We connect these clusters with two types of directed edges to indicate (1) that constituent ORFs are adjacent in one or more genomes, and (2) that these ORFs overlap. These adjacency graphs not only provide a natural visualization scheme, but also a novel statistical framework for analyzing the effects of gene- and genome-level attributes on the frequencies of overlaps.

Nursing in the spotlight: Talk about nurses and the nursing profession on Twitter during the early COVID-19 pandemic

BackgroundNurses comprise the largest portion of healthcare workers and are integral to the COVID-19 response. Twitter has become a popular platform for the public, including nurses, to engage in pandemic-related discourse. PurposeWe sought to analyze the representation of the nursing profession and characterize nurses’ experiences during the pandemic from tweets published in April 2020. MethodsWe analyzed tweets using natural language processing, Word Adjacency Graph (WAG) Modeling, and thematic analysis. Authors independently reviewed 10% of raw tweets in each WAG-generated topic, qualitatively analyzed tweets, and identified emerging themes. FindingsSix themes emerged: Support and Recognition of Nurses, Military Metaphors, Superhuman/Spiritual Metaphors, Advocacy, Personal Experiences with Nurses, and Social/Political Commentary. Public perception of nurses was positive, but nurses conveyed harsh realities of their work. DiscussionFindings highlight discrepancies in nursing experiences and public perceptions of nursing. Further research should accurately identify and convey the complexities of the nursing profession.

Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting

Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people’s daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spatiotemporal graph neural network has attracted attention from academic and business domains for its powerful spatiotemporal pattern capturing capability. However, the existing work focused on the overall traffic network instead of traffic nodes, and the latter can be useful in learning different patterns among nodes. Moreover, there are few works that captured fine-grained node-specific spatiotemporal feature extraction at multiple scales at the same time. To unfold the node pattern, a node embedding parameter was designed to adaptively learn nodes patterns in adjacency matrix and graph convolution layer. To address this multi-scale problem, we adopted the idea of Res2Net and designed a hierarchical temporal attention layer and hierarchical adaptive graph convolution layer. Based on the above methods, a novel model, called Temporal Residual II Graph Convolutional Network (Tres2GCN), was proposed to capture not only multi-scale spatiotemporal but also fine-grained features. Tres2GCN was validated by comparing it with 10 baseline methods using two public traffic volume datasets. The results show that our model performs good accuracy, outperforming existing methods by up to 9.4%.

An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

• Unsupervised segmentation of polyps in colonoscopic images. • Superpixel level grouping of pixels using simple linear iterative clustering (SLIC). • Deployment of adaptive Markov random field (MRF) to stitch homogeneous regions with region adjacency graph (RAG). • Polyp segmentation efficiency of 60.77% is achieved in terms of dice co-efficient. In this article, an unsupervised image segmentation method is proposed to segment out polyps in endoscopic images. Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed. The polyps or abnormal regions show markedly different texture and color characteristics in contrast to normal tissues. In our method, the endoscope images are first over-segmented into superpixels . For final refinement, Local binary pattern (LBP) and color features are used in the adaptive MRF. The experimental results show that the proposed method can perfectly localize polyp regions, and it can give a mean dice value of 60.77% in a test set of 612 images taken from 29 different video sequences. Deep learning models require a huge number of training images for tweaking of network parameters. In this context, it is sometimes challenging to deploy a deep model for unseen medical datasets having many segmentation challenges, like complex background, specularity, non-uniform illuminations, etc. Also, manual labeling of a huge number of images is another challenge. That is why we adopted an unsupervised approach in this paper.

Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

Adaptive Spatio-temporal Graph Neural Network for traffic forecasting

Accurate traffic forecasting is of vital importance for the management and decision in intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic conditions due to the complexity of spatial relationships and temporal dependencies. Recent research developed Spatio-Temporal Graph Neural Networks (ST-GNNs) to capture the spatio-temporal correlations and achieved superior performance. However, the graph adjacency matrices that most ST-GNNs use are either pre-defined by heuristic rules or directly learned with trainable parameters. While node attributes, which record valuable information of traffic conditions, have not been fully exploited to guide the learning of better graph structure. In this paper, we propose an Adaptive Spatio-Temporal graph neural Network, namely Ada-STNet, to first derive optimal graph structure with the guidance of node attributes and then capture the complicated spatio-temporal correlations via a dedicated spatio-temporal convolution architecture for multi-step traffic condition forecasting. Specifically, we first propose a graph structure learning component to obtain an optimal graph adjacency matrix from both macro and micro perspectives. Next, we design a dedicated spatio-temporal convolution architecture to learn spatial relationships and temporal dependencies. Moreover, we present a two-stage training strategy to improve the model performance. Extensive experimental results on real-world datasets demonstrate the effectiveness and interpretability of our approach.

Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding

Superpixel segmentation has become a crucial pre-processing tool to reduce computation in many computer vision applications. In this paper, a superpixel extraction algorithm based on a seed strategy of contour encoding (SSCE) for infrared images is presented, which can generate superpixels with high boundary adherence and compactness. Specifically, SSCE can solve the problem of superpixels being unable to self-adapt to the image content. First, a contour encoding map is obtained by ray scanning the binary edge map, which ensures that each connected domain belongs to the same homogeneous region. Second, according to the seed sampling strategy, each seed point can be extracted from the contour encoding map. The initial seed set, which is adaptively scattered based on the local structure, is capable of improving the capability of boundary adherence, especially for small regions. Finally, the initial superpixels limited by the image contour are generated by clustering and refined by merging similar adjacent superpixels in the region adjacency graph (RAG) to reduce redundant superpixels. Experimental results on a self-built infrared dataset and the public datasets BSD500 and 3Dircadb demonstrate the generalization ability in grayscale and medical images, and the superiority of the proposed method over several state-of-the-art methods in terms of accuracy and compactness.

Spatial-Spectral Clustering With Anchor Graph for Hyperspectral Image

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graph-based clustering methods, which construct an adjacent graph to model the data relationship, have shown dominant performance. However, the high dimensionality of HSI data makes it hard to construct the pairwise adjacent graph. Besides, abundant spatial structures are often overlooked during the clustering procedure. In order to better handle the high dimensionality problem and preserve the spatial structures, this paper proposes a novel unsupervised approach called spatial-spectral clustering with anchor graph (SSCAG) for HSI data clustering. The SSCAG has the following contributions: 1) the anchor graph-based strategy is used to construct a tractable large graph for HSI data, which effectively exploits all data points and reduces the computational complexity; 2) a new similarity metric is presented to embed the spatial-spectral information into the combined adjacent graph, which can mine the intrinsic property structure of HSI data; 3) an effective neighbors assignment strategy is adopted in the optimization, which performs the singular value decomposition (SVD) on the adjacent graph to get solutions efficiently. Extensive experiments on three public HSI datasets show that the proposed SSCAG is competitive against the state-of-the-art approaches.

Graph Convolutional Enhanced Discriminative Broad Learning System for Hyperspectral Image Classification

Recently, broad learning system (BLS) have demonstrated excellent performance in hyperspectral images (HSI) classification. However, due to the complex geometric structure and spatial layout of HSI, the linear sparse features in broad learning system are difficult to fully represent hyperspectral data. In addition, the features learned by broad learning system lack more effective discriminative ability, which leads to the limited expressive ability of features. To address the issues, we propose a graph convolutional enhanced discriminative broad learning system (GCDBLS) for HSI Classification. GCDBLS aggregates the node information in the adjacency graph through graph convolution, and then learns the context relationship, so as to obtain rich nonlinear spatial spectral features in hyperspectral images; In order to extract more discriminative hyperspectral image features, GCDBLS introduces the concept of local intra-class scatter and local inter-class scatter. By minimizing the local intra-class feature distance and maximizing the local inter-class feature distance, GCDBLS can improve the discrimination ability of BLS extracted features. On three HSI datasets, the experiments compared with the latest classification methods show that the proposed method achieves good results, and improves the classification performance of hyperspectral images.

Graph Convolutional Networks for Semi-Supervised Image Segmentation

The problem of image segmentation is one of the most significant ones in computer vision. Recently, deep-learning methods have dominated state-of-the-art solutions that automatically or interactively divide an image into subregions. However, the limitation of deep-learning approaches is that they require a substantial amount of training data, which is costly to prepare. An alternative solution is semi-supervised image segmentation. It requires rough denotations to define constraints that are next generalized to precisely delimit relevant image regions without using train examples. Among semi-supervised strategies for image segmentation, the leading are graph-based techniques that define image segmentation as a result of pixel or region affinity graph partitioning. This paper revisits the problem of graph-based image segmentation. It approaches the problem as semi-supervised node classification in the SLIC superpixels region adjacency graph using a graph convolutional network (GCN). The performance of both spectral and spatial graph convolution operators is considered, represented by Chebyshev convolution operator and GraphSAGE respectively. The results of the proposed method applied to binary and multi-label segmentation are presented, numerically assessed, and analyzed. In its best variant, the proposed method scored the average DICE of 0.86 in the binary segmentation task and 0.79 in the multi-label segmentation task. Comparison with state-of-the-art graph-based methods, including Random Walker and GrabCut, shows that graph convolutional networks can represent an attractive alternative to the existing solutions to graph-based semi-supervised image segmentation.

Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Laplacian Regularized Collaborative Representation Projection

Hyperspectral images (HSIs) consisting of abundant spectral bands could lead to the curse of dimensionality issue when performing HSIs classification. In this letter, an unsupervised dimensionality reduction (DR) method termed Laplacian regularized collaborative representation projection (LRCRP) is proposed, where Laplacian regularization and local enhancement are introduced into collaborative representation (CR) to construct adjacent graph and then to reduce the spectral dimension in graph embedding framework. As the constructed graph simultaneously preserves the local manifold and global information in HSIs, the proposed LRCRP could be used to extract effective low-dimensional features for accurate HSIs classification. The experimental results on two HSI datasets demonstrate the effectiveness of the proposed model. The source code the proposed model is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XinweiJiang/LRCRP</uri> .

Hyperspectral Imagery Classification via Random Multigraphs Ensemble Learning

Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semisupervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limited labeled data for classification task. However, it is hard to construct the pairwise adjacent graph due to the high dimensionality of hyperspectral data. Besides, the graph-based SSL models are usually decided by a single classifier, which fail to effectively learn the complex structures and intrinsic properties of HSI. To address these problems, we propose a novel graph-based SSL classification model for HSI, which is based on random multigraphs construction and ensemble strategy (RMGE). Specifically, the anchor graph (AG) is constructed with spatial–spectral features, which integrates the spatial characteristics extracted by local binary pattern on each selected spectrum, preserving fine structures of local region. In order to enhance the discriminative capability of the classifier and avoid the trivial solution, the maximum entropy regularization is added into adjacent AG model. In addition, to capture the diversity of HSI data effectively, we design the ensemble framework by employing multiple AGs to learn HSI features. Experiments conducted on real hyperspectral datasets indicate that the proposed RMGE shows better performance than that of state-of-the-art approaches.

The Adjacency Graphs of FSRs With Affine Characteristic Functions

We study the adjacency graphs of feedback shift registers (FSRs) with affine characteristic functions. We first show that, similar to the linear case, the output sequences of FSRs with affine characteristic functions also have the direct sum decompositions. The only difference from the linear case is that one component of the decomposition is a family of affine sequences, rather than linear sequences. Then, based on this fact, we give a relationship between the adjacency graphs of FSRs with affine characteristic functions and the generalized adjacency graphs of FSRs whose characteristic functions correspond to the components of the decomposition. This relationship establishes the algebraic structure of adjacency graphs and greatly simplifies the calculation of them. At last, we especially study the adjacency graph of the FSR whose characteristic function is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{k}(x)+1$ </tex-math></inline-formula> where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q_{k}(x)$ </tex-math></inline-formula> is the linear function corresponding to the polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1+x)^{k}$ </tex-math></inline-formula> . We prove that for any two cycles of this FSR, the number of conjugate pairs shared by them is no more than 4 if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a power of 2, and no more than 2 if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is not a power of 2.

A Novel Approach for Multiscale Lunar Crater Detection by the Use of Path-Profile and Isolation Forest Based on High-Resolution Planetary Images

Crater detection from planetary images is a challenging issue due to the complicated variations in geometry shape, illumination, and scale. An automatic crater detection algorithm (CDA) that is robust to these factors is, therefore, necessary. In this article, a novel automatic CDA that is robust to these factors is proposed to detect the multiscale craters of the Moon. The proposed method consists of two main steps: 1) in the hypothesis generation (HG) step, a novel feature operator called the path-profile, which is constructed based on the self-defined adjacency graph and a path descriptor, is presented to derive the highlight-shadow feature of craters for detecting candidate craters. 2) In the hypothesis verification (HV) step, based on the idea of anomaly detection, the isolation forest algorithm which is an unsupervised learning anomaly detection method is applied to eliminate falsely detected craters. Lunar Reconnaissance Orbiter Camera Wide Angle Camera and Narrow Angle Camera images and Chang&#x2019;E-4 landing camera images were used to test the accuracy and robustness of the proposed method. The experimental results indicate that: on average, the accuracy of the detection result of the HG step is about 90&#x0025;, and the HV step can further improve this by 3&#x0025;&#x2013;4&#x0025;. The proposed method is a reliable way to detect multiscale lunar craters for various resolutions images with diameters ranging from five pixels to hundreds of pixels, and it is robust to the different terrains and illumination conditions on the Moon.

Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval

Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured, along with their region-wise features in their region adjacency graphs. Also, the attention mechanism has often been applied to the nodes to highlight the essential features in each node. In this regard, a significant amount of high-frequency information is missed since each image segment is effectively summarized within a single node. To account for this and increase the learning capacity, we propose to attend over the edge/adjacency matrix to highlight the interactions among meaningful regions that contribute to supervised learning from images. We exploit this novel edge attention mechanism together with node attention to highlight essential image context by allowing more importance to the meaningful neighboring regions that highlight a relevant node. We implement the proposed context-attended GCN framework for image retrieval on the benchmarked UC-Merced and the PatternNet datasets. We observe a notable improvement in the results compared to the state of the art.

Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos

For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information. To solve this, we propose a novel weakly supervised adaptive graph convolutional network (WAGCN) to model the complex contextual relationship among video segments. By which, we fully consider the influence of other video segments on the current one when generating the anomaly probability score for each segment. Firstly, we combine the temporal consistency as well as feature similarity of video segments to construct a global graph, which makes full use of the association information among spatial-temporal features of anomalous events in videos. Secondly, we propose a graph learning layer in order to break the limitation of setting topology manually, which can extract graph adjacency matrix based on data adaptively and effectively. Extensive experiments on two public datasets (i.e., UCF-Crime dataset and ShanghaiTech dataset) demonstrate the effectiveness of our approach which achieves state-of-the-art performance.

On the Fractionalization of the Shift Operator on Graphs

The theory of graph signal processing has been established with the purpose of generalizing tools from classical digital signal processing to the cases where the signal domain can be modeled by an arbitrary graph. In this context, the present paper introduces the notion of fractional shift of signals on graphs, which is related to considering a non-integer power of the graph adjacency matrix. Among the results derived throughout this work, we prove that the referred fractional operator can be implemented as a linear and shift-invariant graph filter for any graph and verify its convergence to the classical fractional delay when a directed ring graph is considered. By means of a real-world example, we show that, using the proposed operator, graph filters that approximate an ideal filter better than those designed using the ordinary adjacency matrix can be obtained. An additional example dealing with noise removal from graph signals illustrates the gain provided by the mentioned filter design strategy.

Solving Graph Coloring Problem Using Graph Adjacency Matrix Algorithm

Solving Graph Coloring Problem Using Graph Adjacency Matrix Algorithm

Multi granularity based label propagation with active learning for semi-supervised classification

Semi-supervised learning (SSL) methods, which exploit both the labeled and unlabeled data, have attracted a lot of attention. One of the major categories of SSL methods, graph-based semi-supervised learning (GBSSL) learns labels of unlabeled data on an adjacency graph, where neighborhood sparse graph is often used to reduce computational complexity. However, the neighborhood size is difficult to set. Instead of assigning a concrete value of neighborhood size, we propose a new label propagation algorithm called multi granularity based label propagation (MGLP) and developed from the view of granular computing. In MGLP, labels of unlabeled data are learned by two classic label propagation processes with diverse neighborhood size k, where granular computing delivers a guiding strategy to leverage multiple level neighborhood information granules, and three-way decision acts as an active learning strategy to select the unlabeled data for further annotating. Through the iterative procedures of label propagating, data annotating and data subset updating, the ultimate pseudo label accuracy of unlabeled data may be higher. Theoretically, the accuracy of pseudo labels is enhanced in some scenarios. Experimentally, the results of simulation studies on ten benchmark datasets, show that the proposed method MGLP can rise pseudo labels accuracy by 8.6% than LP (label propagation), 6.5% than LNP (linear neighborhood propagation), 6.4% than LPSN (label propagation through sparse neighborhood), 4.5% than Adaptive-NP (adaptive neighborhood propagation) and 4.6% than CRLP (consensus rate-based label propagation). It also provides a novel way to annotate data.

Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction.

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data’s three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.