scholarly journals Adversarial Deep Network Embedding for Cross-Network Node Classification

2020 ◽  
Vol 34 (03) ◽  
pp. 2991-2999 ◽  
Author(s):  
Xiao Shen ◽  
Quanyu Dai ◽  
Fu-lai Chung ◽  
Wei Lu ◽  
Kup-Sze Choi
Keyword(s):  
Domain Adaptation ◽  
Structural Information ◽  
Classification Problem ◽  
Network Node ◽  
Network Embedding ◽  
Deep Network ◽  
Network Applications ◽  
Target Network ◽  
Node Classification ◽  
Learn Network

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

scholarly journals Network Together: Node Classification via Cross-Network Deep Network Embedding

2020 ◽  
pp. 1-14
Author(s):  
Xiao Shen ◽  
Quanyu Dai ◽  
Sitong Mao ◽  
Fu-Lai Chung ◽  
Kup-Sze Choi
Keyword(s):  
Network Embedding ◽  
Deep Network ◽  
Node Classification

scholarly journals An aggregate method for thorax diseases classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bayu Adhi Nugroho
Keyword(s):  
Network Architecture ◽  
Classification Problem ◽  
Calculation Algorithm ◽  
Training Pattern ◽  
Deep Network ◽  
Network Training ◽  
Chest X Ray ◽  
Medical Image Classification ◽  
Positive Pattern

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.

Network Embedding on Hierarchical Community Structure Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Network Properties ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

scholarly journals Principled approach to the selection of the embedding dimension of networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Aditya Tandon ◽  
Yong-Yeol Ahn ◽  
Filippo Radicchi
Keyword(s):  
Real World ◽  
Structural Information ◽  
General Purpose ◽  
Embedding Dimension ◽  
Network Embedding ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Low Dimensional ◽  
Large Corpus ◽  
Selection Of

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

SVM-BASED METHOD FOR PROTEIN STRUCTURAL CLASS PREDICTION USING SECONDARY STRUCTURAL CONTENT AND STRUCTURAL INFORMATION OF AMINO ACIDS

2011 ◽  
Vol 09 (04) ◽  
pp. 489-502 ◽  
Author(s):  
TABREZ ANWAR SHAMIM MOHAMMAD ◽  
HAMPAPATHALU ADIMURTHY NAGARAJARAM
Keyword(s):  
Amino Acids ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Classification Problem ◽  
Support Vector ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Structural Content

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.

scholarly journals Hierarchical Multi Twin Support Vector Machine

2021 ◽  
Vol 130 (2B) ◽  
Author(s):  
Nguyen The Cuong
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Data Type ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

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