adjacency graph
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2022 ◽  
Vol 12 (4) ◽  
pp. 807-812
Author(s):  
Yan Li ◽  
Yu-Ren Zhang ◽  
Ping Zhang ◽  
Dong-Xu Li ◽  
Tian-Long Xiao

It is a critical impact on the processing of biological cells to protein–protein interactions (PPIs) in nature. Traditional PPIs predictive biological experiments consume a lot of human and material costs and time. Therefore, there is a great need to use computational methods to forecast PPIs. Most of the existing calculation methods are based on the sequence characteristics or internal structural characteristics of proteins, and most of them have the singleness of features. Therefore, we propose a novel method to predict PPIs base on multiple information fusion through graph representation learning. Specifically, firstly, the known protein sequences are calculated, and the properties of each protein are obtained by k-mer. Then, the known protein relationship pairs were constructed into an adjacency graph, and the graph representation learning method–graph convolution network was used to fuse the attributes of each protein with the graph structure information to obtain the features containing a variety of information. Finally, we put the multi-information features into the random forest classifier species for prediction and classification. Experimental results indicate that our method has high accuracy and AUC of 78.83% and 86.10%, respectively. In conclusion, our method has an excellent application prospect for predicting unknown PPIs.


2021 ◽  
Author(s):  
Shinsuke Kajioka ◽  
Takanori Saito ◽  
Daisuke Yamamoto ◽  
Naohisa Takahashi
Keyword(s):  

2021 ◽  
Vol 13 (14) ◽  
pp. 2752
Author(s):  
Na Li ◽  
Deyun Zhou ◽  
Jiao Shi ◽  
Tao Wu ◽  
Maoguo Gong

Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.


Author(s):  
H. Zavar ◽  
H. Arefi ◽  
S. Malihi ◽  
M. Maboudi

Abstract. In this paper we introduce a topology-aware data-driven approach for 3D reconstruction of indoor spaces, which is an active research topic with several practical applications. After separating floor and ceiling, segmentation is followed by computing the α-shapes of the segment. The adjacency graph of all α-shapes is used to find the intersecting planes. By employing a B-rep approach, an initial 3D model is computed. Afterwards, adjacency graph of the intersected planes which constitute the initial model is analyzed in order to refine the 3D model. This leads to a water-tight and topologically correct 3D model. The performance of our proposed approach is qualitatively and quantitatively evaluated on an ISPRS benchmark data set. On this dataset, we achieved 77% completeness, 53% correctness and 1.7–5 cm accuracy with comparison of the final 3D model to the ground truth.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zheng Wang ◽  
Yuexin Wu ◽  
Yang Bao ◽  
Jing Yu ◽  
Xiaohui Wang

Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.


2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.


2021 ◽  
Vol 16 ◽  
pp. 135
Author(s):  
V.A. Perepelitsa ◽  
I.V. Kozin ◽  
S.V. Kurapov

We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.


Author(s):  
Jaeyoung Song ◽  
Kiyun Yu

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.


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