scholarly journals Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network

2021 ◽  
Vol 2 (1) ◽  
pp. 43-51
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Ruiqi Li ◽  
Jiang Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Link Prediction ◽  
Network Representation ◽  
Universal Network

scholarly journals Link Prediction Based on Deep Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 172
Author(s):  
Wentao Wang ◽  
Lintao Wu ◽  
Ye Huang ◽  
Hao Wang ◽  
Rongbo Zhu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Link Prediction ◽  
Prediction Method ◽  
Representation Learning ◽  
Deep Convolutional Neural Network ◽  
Published Data ◽  
Neighborhood Structure ◽  
Structure Information ◽  
Network Representation

In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction.

Convolutional Neural Network Representation of the Total Multiple Scattering Cross Section

2021 ◽  
Author(s):  
Thang Tran ◽  
Peter Lai ◽  
Wei-Ching Wang ◽  
Amaris Rosa ◽  
Feruza Amirkulova ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Multiple Scattering ◽  
Cross Section ◽  
Scattering Cross Section ◽  
Scattering Cross ◽  
Network Representation

scholarly journals Identifying Disease Related Genes by Network Representation and Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Bolin Chen ◽  
Yourui Han ◽  
Xuequn Shang ◽  
Shenggui Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Biological Networks ◽  
Dimensional Space ◽  
Scale Free ◽  
Network Representation ◽  
Disease Related Genes ◽  
Representation Method ◽  
Low Dimensional

The identification of disease related genes plays essential roles in bioinformatics. To achieve this, many powerful machine learning methods have been proposed from various computational aspects, such as biological network analysis, classification, regression, deep learning, etc. Among them, deep learning based methods have gained big success in identifying disease related genes in terms of higher accuracy and efficiency. However, these methods rarely handle the following two issues very well, which are (1) the multifunctions of many genes; and (2) the scale-free property of biological networks. To overcome these, we propose a novel network representation method to transfer individual vertices together with their surrounding topological structures into image-like datasets. It takes each node-induced sub-network as a represented candidate, and adds its environmental characteristics to generate a low-dimensional space as its representation. This image-like datasets can be applied directly in a Convolutional Neural Network-based method for identifying cancer-related genes. The numerical experiments show that the proposed method can achieve the AUC value at 0.9256 in a single network and at 0.9452 in multiple networks, which outperforms many existing methods.

Unifying Online and Offline Preference for Social Link Prediction

2021 ◽  
Author(s):  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Bangying Wu ◽  
Yi Yang ◽  
Harry Jiannan Wang
Keyword(s):  
Neural Network ◽  
Social Relations ◽  
Link Prediction ◽  
Factor Model ◽  
Representation Learning ◽  
Machine Learning Algorithms ◽  
Locality Sensitive Hashing ◽  
Network Representation ◽  
Mobility Data ◽  
Social Link

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

scholarly journals SVD-CNN: A Convolutional Neural Network Model with Orthogonal Constraints Based on SVD for Context-Aware Citation Recommendation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shaoyu Tao ◽  
Chaoyuan Shen ◽  
Li Zhu ◽  
Tao Dai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Link Prediction ◽  
Difficult Problem ◽  
Weight Matrix ◽  
Context Aware ◽  
Citation Context ◽  
Scientific Papers ◽  
Interactional Features

Context-aware citation recommendation aims to automatically predict suitable citations for a given citation context, which is essentially helpful for researchers when writing scientific papers. In existing neural network-based approaches, overcorrelation in the weight matrix influences semantic similarity, which is a difficult problem to solve. In this paper, we propose a novel context-aware citation recommendation approach that can essentially improve the orthogonality of the weight matrix and explore more accurate citation patterns. We quantitatively show that the various reference patterns in the paper have interactional features that can significantly affect link prediction. We conduct experiments on the CiteSeer datasets. The results show that our model is superior to baseline models in all metrics.

scholarly journals Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

2019 ◽  
Vol 38 (3) ◽  
pp. 675-685 ◽  
Author(s):  
Kuang Gong ◽  
Jiahui Guan ◽  
Kyungsang Kim ◽  
Xuezhu Zhang ◽  
Jaewon Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Network Representation ◽  
Pet Image Reconstruction

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Sign in / Sign up

Export Citation Format

Share Document